CINXE.COM

<html> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=UA-131826476-1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-131826476-1'); </script> <meta http-equiv="Content-type" content="text/html;charset=UTF-8">  <link rel="icon" href="/img/favicon.ico"> <link rel="icon" type="image/png" href="/img/favicon-16x16.png"> <link rel="icon" type="image/png" href="/img/favicon-32x32.png"> <title>Journal of Machine Learning Research</title> <link rel="alternate" type="application/rss+xml" href="/jmlr.xml" title="JMLR RSS"> <link rel="stylesheet" type="text/css" href="/style.css"> <style type="text/css"> . {font-family:verdana,helvetica,sans-serif} a {text-decoration:none;color:#3030a0} #fixed { position: absolute; top: 0; left: 0; width: 8em; height: 100%; } body > #fixed { position: fixed; } #content { margin-top: 1em; margin-left: 10em; margin-right: 0.5em; } img.jmlr { width: 7em; } img.rss { width: 2em; } ul li { margin-bottom: 0.5em; } </style> </head> <body> <div id="fixed"> <a align="right" href="/" target=_top><img align="right" class="jmlr" src="/img/jmlr.jpg" border="0"></a> <A href="/"> Home Page </A> <A href="/papers"> Papers </A> <A href="/author-info.html"> Submissions </A> <A href="/news.html"> News </A>  <A href="/editorial-board.html"> Editorial Board </A> <A href="/special_issues/"> Special Issues </A> <A href="/mloss">Open Source Software</A> <A href="https://proceedings.mlr.press/"> Proceedings (PMLR)</A> <A href="https://data.mlr.press/"> Data (DMLR) </A> <A href="/tmlr"> Transactions (TMLR) </A> <A href="/search-jmlr.html"> Search </A> <A href="/stats.html">Statistics</A> <A href="/manudb"> Login </A> <A href="/faq.html">Frequently Asked Questions </A> <A href="/contact.html"> Contact Us </A> <A href="/jmlr.xml"> <img src="/img/RSS.gif" class="rss" alt="RSS Feed"> </A> </div> <div id="content"> <h1>Machine Learning Open Source Software</h1> To support the open source software movement, JMLR MLOSS publishes contributions related to implementations of non-trivial machine learning algorithms, toolboxes or even languages for scientific computing. Submission instructions are available <a href="mloss-info.html">here</a>. <dl> <dt>Aequitas Flow: Streamlining Fair ML Experimentation</dt> <dd>S茅rgio Jesus, Pedro Saleiro, In锚s Oliveira e Silva, Beatriz M. Jorge, Rita P. Ribeiro, Jo茫o Gama, Pedro Bizarro, Rayid Ghani; (354):1−7, 2024. [<a href='/papers/v25/24-0677.html'>abs</a>][<a target=_blank href='/papers/volume25/24-0677/24-0677.pdf'>pdf</a>][<a href="/papers/v25/24-0677.bib">bib</a>]      [<a href="https://github.com/dssg/aequitas">code</a>] </dl> <dl> <dt>Open-Source Conversational AI with SpeechBrain 1.0</dt> <dd>Mirco Ravanelli, Titouan Parcollet, Adel Moumen, Sylvain de Langen, Cem Subakan, Peter Plantinga, Yingzhi Wang, Pooneh Mousavi, Luca Della Libera, Artem Ploujnikov, Francesco Paissan, Davide Borra, Salah Zaiem, Zeyu Zhao, Shucong Zhang, Georgios Karakasidis, Sung-Lin Yeh, Pierre Champion, Aku Rouhe, Rudolf Braun, Florian Mai, Juan Zuluaga-Gomez, Seyed Mahed Mousavi, Andreas Nautsch, Ha Nguyen, Xuechen Liu, Sangeet Sagar, Jarod Duret, Salima Mdhaffar, Ga毛lle Laperri猫re, Mickael Rouvier, Renato De Mori, Yannick Est猫ve; (333):1−11, 2024. [<a href='/papers/v25/24-0991.html'>abs</a>][<a target=_blank href='/papers/volume25/24-0991/24-0991.pdf'>pdf</a>][<a href="/papers/v25/24-0991.bib">bib</a>]      [<a href="https://speechbrain.github.io/">code</a>] </dl> <dl> <dt>RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control</dt> <dd>Jonas Eschmann, Dario Albani, Giuseppe Loianno; (301):1−19, 2024. [<a href='/papers/v25/24-0248.html'>abs</a>][<a target=_blank href='/papers/volume25/24-0248/24-0248.pdf'>pdf</a>][<a href="/papers/v25/24-0248.bib">bib</a>]      [<a href="https://github.com/rl-tools/rl-tools">code</a>] </dl> <dl> <dt>PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization</dt> <dd>Qiqi Duan, Guochen Zhou, Chang Shao, Zhuowei Wang, Mingyang Feng, Yuwei Huang, Yajing Tan, Yijun Yang, Qi Zhao, Yuhui Shi; (296):1−28, 2024. [<a href='/papers/v25/23-0386.html'>abs</a>][<a target=_blank href='/papers/volume25/23-0386/23-0386.pdf'>pdf</a>][<a href="/papers/v25/23-0386.bib">bib</a>]      [<a href="https://github.com/Evolutionary-Intelligence/pypop">code</a>] </dl> <dl> <dt>skscope: Fast Sparsity-Constrained Optimization in Python</dt> <dd>Zezhi Wang, Junxian Zhu, Xueqin Wang, Jin Zhu, Huiyang Pen, Peng Chen, Anran Wang, Xiaoke Zhang; (290):1−9, 2024. [<a href='/papers/v25/23-1574.html'>abs</a>][<a target=_blank href='/papers/volume25/23-1574/23-1574.pdf'>pdf</a>][<a href="/papers/v25/23-1574.bib">bib</a>]      [<a href="https://github.com/abess-team/skscope">code</a>] </dl> <dl> <dt>aeon: a Python Toolkit for Learning from Time Series</dt> <dd>Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo-Rubio, Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Sch盲fer, Anthony Bagnall; (289):1−10, 2024. [<a href='/papers/v25/23-1444.html'>abs</a>][<a target=_blank href='/papers/volume25/23-1444/23-1444.pdf'>pdf</a>][<a href="/papers/v25/23-1444.bib">bib</a>]      [<a href="https://github.com/aeon-toolkit/aeon">code</a>] </dl> <dl> <dt>OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research</dt> <dd>Jiaming Ji, Jiayi Zhou, Borong Zhang, Juntao Dai, Xuehai Pan, Ruiyang Sun, Weidong Huang, Yiran Geng, Mickel Liu, Yaodong Yang; (285):1−6, 2024. [<a href='/papers/v25/23-0681.html'>abs</a>][<a target=_blank href='/papers/volume25/23-0681/23-0681.pdf'>pdf</a>][<a href="/papers/v25/23-0681.bib">bib</a>]      [<a href="https://github.com/PKU-Alignment/omnisafe">code</a>] </dl> <dl> <dt>Pearl: A Production-Ready Reinforcement Learning Agent</dt> <dd>Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari, Daniel Jiang, Yi Wan, Yonathan Efroni, Liyuan Wang, Ruiyang Xu, Hongbo Guo, Alex Nikulkov, Dmytro Korenkevych, Urun Dogan, Frank Cheng, Zheng Wu, Wanqiao Xu; (273):1−30, 2024. [<a href='/papers/v25/24-0196.html'>abs</a>][<a target=_blank href='/papers/volume25/24-0196/24-0196.pdf'>pdf</a>][<a href="/papers/v25/24-0196.bib">bib</a>]      [<a href="http://github.com/facebookresearch/pearl">code</a>] </dl> <dl> <dt>pgmpy: A Python Toolkit for Bayesian Networks</dt> <dd>Ankur Ankan, Johannes Textor; (265):1−8, 2024. [<a href='/papers/v25/23-0487.html'>abs</a>][<a target=_blank href='/papers/volume25/23-0487/23-0487.pdf'>pdf</a>][<a href="/papers/v25/23-0487.bib">bib</a>]      [<a href="https://github.com/pgmpy">code</a>] </dl> <dl> <dt>PromptBench: A Unified Library for Evaluation of Large Language Models</dt> <dd>Kaijie Zhu, Qinlin Zhao, Hao Chen, Jindong Wang, Xing Xie; (254):1−22, 2024. [<a href='/papers/v25/24-0023.html'>abs</a>][<a target=_blank href='/papers/volume25/24-0023/24-0023.pdf'>pdf</a>][<a href="/papers/v25/24-0023.bib">bib</a>]      [<a href="https://github.com/microsoft/promptbench">code</a>] </dl> <dl> <dt>Fortuna: A Library for Uncertainty Quantification in Deep Learning</dt> <dd>Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias Seeger, Andrew Gordon Wilson, Cedric Archambeau; (238):1−7, 2024. [<a href='/papers/v25/23-0145.html'>abs</a>][<a target=_blank href='/papers/volume25/23-0145/23-0145.pdf'>pdf</a>][<a href="/papers/v25/23-0145.bib">bib</a>]      [<a href="https://github.com/awslabs/fortuna">code</a>] </dl> <dl> <dt>BenchMARL: Benchmarking Multi-Agent Reinforcement Learning</dt> <dd>Matteo Bettini, Amanda Prorok, Vincent Moens; (217):1−10, 2024. [<a href='/papers/v25/23-1612.html'>abs</a>][<a target=_blank href='/papers/volume25/23-1612/23-1612.pdf'>pdf</a>][<a href="/papers/v25/23-1612.bib">bib</a>]      [<a href="https://github.com/facebookresearch/BenchMARL">code</a>] </dl> <dl> <dt>PAMI: An Open-Source Python Library for Pattern Mining</dt> <dd>Uday Kiran Rage, Veena Pamalla, Masashi Toyoda, Masaru Kitsuregawa; (209):1−6, 2024. [<a href='/papers/v25/22-1026.html'>abs</a>][<a target=_blank href='/papers/volume25/22-1026/22-1026.pdf'>pdf</a>][<a href="/papers/v25/22-1026.bib">bib</a>]      [<a href="https://github.com/UdayLab/PAMI">code</a>] </dl> <dl> <dt>DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models</dt> <dd>Patrick Bl枚baum, Peter G枚tz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing; (147):1−7, 2024. [<a href='/papers/v25/22-1258.html'>abs</a>][<a target=_blank href='/papers/volume25/22-1258/22-1258.pdf'>pdf</a>][<a href="/papers/v25/22-1258.bib">bib</a>]      [<a href="https://github.com/py-why/dowhy">code</a>] </dl> <dl> <dt>PyGOD: A Python Library for Graph Outlier Detection</dt> <dd>Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang, Hao Peng, Lichao Sun, Philip S. Yu; (141):1−9, 2024. [<a href='/papers/v25/23-0963.html'>abs</a>][<a target=_blank href='/papers/volume25/23-0963/23-0963.pdf'>pdf</a>][<a href="/papers/v25/23-0963.bib">bib</a>]      [<a href="https://pygod.org">code</a>] </dl> <dl> <dt>OpenBox: A Python Toolkit for Generalized Black-box Optimization</dt> <dd>Huaijun Jiang, Yu Shen, Yang Li, Beicheng Xu, Sixian Du, Wentao Zhang, Ce Zhang, Bin Cui; (120):1−11, 2024. [<a href='/papers/v25/23-0537.html'>abs</a>][<a target=_blank href='/papers/volume25/23-0537/23-0537.pdf'>pdf</a>][<a href="/papers/v25/23-0537.bib">bib</a>]      [<a href="https://github.com/PKU-DAIR/open-box">code</a>] </dl> <dl> <dt>QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration</dt> <dd>Felix Chalumeau, Bryan Lim, Rapha毛l Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Mac茅, Guillaume Richard, Arthur Flajolet, Thomas Pierrot, Antoine Cully; (108):1−16, 2024. [<a href='/papers/v25/23-1027.html'>abs</a>][<a target=_blank href='/papers/volume25/23-1027/23-1027.pdf'>pdf</a>][<a href="/papers/v25/23-1027.bib">bib</a>]      [<a href="https://github.com/adaptive-intelligent-robotics/QDax">code</a>] </dl> <dl> <dt>ptwt - The PyTorch Wavelet Toolbox</dt> <dd>Moritz Wolter, Felix Blanke, Jochen Garcke, Charles Tapley Hoyt; (80):1−7, 2024. [<a href='/papers/v25/23-0636.html'>abs</a>][<a target=_blank href='/papers/volume25/23-0636/23-0636.pdf'>pdf</a>][<a href="/papers/v25/23-0636.bib">bib</a>]      [<a href="https://github.com/v0lta/PyTorch-Wavelet-Toolbox">code</a>] </dl> <dl> <dt>On Unbiased Estimation for Partially Observed Diffusions</dt> <dd>Jeremy Heng, Jeremie Houssineau, Ajay Jasra; (66):1−66, 2024. [<a href='/papers/v25/23-0347.html'>abs</a>][<a target=_blank href='/papers/volume25/23-0347/23-0347.pdf'>pdf</a>][<a href="/papers/v25/23-0347.bib">bib</a>]      [<a href="https://github.com/jeremyhengjm/UnbiasedScore">code</a>] </dl> <dl> <dt>Causal-learn: Causal Discovery in Python</dt> <dd>Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang; (60):1−8, 2024. [<a href='/papers/v25/23-0970.html'>abs</a>][<a target=_blank href='/papers/volume25/23-0970/23-0970.pdf'>pdf</a>][<a href="/papers/v25/23-0970.bib">bib</a>]      [<a href="https://github.com/py-why/causal-learn">code</a>] </dl> <dl> <dt>Invariant and Equivariant Reynolds Networks</dt> <dd>Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai; (42):1−36, 2024. [<a href='/papers/v25/22-0891.html'>abs</a>][<a target=_blank href='/papers/volume25/22-0891/22-0891.pdf'>pdf</a>][<a href="/papers/v25/22-0891.bib">bib</a>]      [<a href="https://github.com/makora9143/ReyNet">code</a>] </dl> <dl> <dt>Pygmtools: A Python Graph Matching Toolkit</dt> <dd>Runzhong Wang, Ziao Guo, Wenzheng Pan, Jiale Ma, Yikai Zhang, Nan Yang, Qi Liu, Longxuan Wei, Hanxue Zhang, Chang Liu, Zetian Jiang, Xiaokang Yang, Junchi Yan; (33):1−7, 2024. [<a href='/papers/v25/23-0572.html'>abs</a>][<a target=_blank href='/papers/volume25/23-0572/23-0572.pdf'>pdf</a>][<a href="/papers/v25/23-0572.bib">bib</a>]      [<a href="https://github.com/Thinklab-SJTU/pygmtools">code</a>] </dl> <dl> <dt>Scaling Up Models and Data with t5x and seqio</dt> <dd>Adam Roberts, Hyung Won Chung, Gaurav Mishra, Anselm Levskaya, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Kehang Han, Michelle Casbon, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, Andrea Gesmundo; (377):1−8, 2023. [<a href='/papers/v24/23-0795.html'>abs</a>][<a target=_blank href='/papers/volume24/23-0795/23-0795.pdf'>pdf</a>][<a href="/papers/v24/23-0795.bib">bib</a>]      [<a href="https://github.com/google-research/t5x">code</a>] </dl> <dl> <dt>TorchOpt: An Efficient Library for Differentiable Optimization</dt> <dd>Jie Ren*, Xidong Feng*, Bo Liu*, Xuehai Pan*, Yao Fu, Luo Mai, Yaodong Yang; (367):1−14, 2023. [<a href='/papers/v24/23-0191.html'>abs</a>][<a target=_blank href='/papers/volume24/23-0191/23-0191.pdf'>pdf</a>][<a href="/papers/v24/23-0191.bib">bib</a>]      [<a href="https://github.com/metaopt/torchopt">code</a>] </dl> <dl> <dt>Avalanche: A PyTorch Library for Deep Continual Learning</dt> <dd>Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco; (363):1−6, 2023. [<a href='/papers/v24/23-0130.html'>abs</a>][<a target=_blank href='/papers/volume24/23-0130/23-0130.pdf'>pdf</a>][<a href="/papers/v24/23-0130.bib">bib</a>]      [<a href="https://avalanche.continualai.org/">code</a>] </dl> <dl> <dt>MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library</dt> <dd>Siyi Hu, Yifan Zhong, Minquan Gao, Weixun Wang, Hao Dong, Xiaodan Liang, Zhihui Li, Xiaojun Chang, Yaodong Yang; (315):1−23, 2023. [<a href='/papers/v24/23-0378.html'>abs</a>][<a target=_blank href='/papers/volume24/23-0378/23-0378.pdf'>pdf</a>][<a href="/papers/v24/23-0378.bib">bib</a>]      [<a href="https://github.com/Replicable-MARL/MARLlib">code</a>] </dl> <dl> <dt>Fairlearn: Assessing and Improving Fairness of AI Systems</dt> <dd>Hilde Weerts, Miroslav Dud铆k, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio; (257):1−8, 2023. [<a href='/papers/v24/23-0389.html'>abs</a>][<a target=_blank href='/papers/volume24/23-0389/23-0389.pdf'>pdf</a>][<a href="/papers/v24/23-0389.bib">bib</a>]      [<a href="https://github.com/fairlearn/fairlearn">code</a>] </dl> <dl> <dt>Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures</dt> <dd>Mike Heddes, Igor Nunes, Pere Verg茅s, Denis Kleyko, Danny Abraham, Tony Givargis, Alexandru Nicolau, Alexander Veidenbaum; (255):1−10, 2023. [<a href='/papers/v24/23-0300.html'>abs</a>][<a target=_blank href='/papers/volume24/23-0300/23-0300.pdf'>pdf</a>][<a href="/papers/v24/23-0300.bib">bib</a>]      [<a href="https://github.com/hyperdimensional-computing/torchhd">code</a>] </dl> <dl> <dt>skrl: Modular and Flexible Library for Reinforcement Learning</dt> <dd>Antonio Serrano-Mu帽oz, Dimitrios Chrysostomou, Simon B酶gh, Nestor Arana-Arexolaleiba; (254):1−9, 2023. [<a href='/papers/v24/23-0112.html'>abs</a>][<a target=_blank href='/papers/volume24/23-0112/23-0112.pdf'>pdf</a>][<a href="/papers/v24/23-0112.bib">bib</a>]      [<a href="https://github.com/Toni-SM/skrl">code</a>] </dl> <dl> <dt>MultiZoo and MultiBench: A Standardized Toolkit for Multimodal Deep Learning</dt> <dd>Paul Pu Liang, Yiwei Lyu, Xiang Fan, Arav Agarwal, Yun Cheng, Louis-Philippe Morency, Ruslan Salakhutdinov; (234):1−7, 2023. [<a href='/papers/v24/22-1021.html'>abs</a>][<a target=_blank href='/papers/volume24/22-1021/22-1021.pdf'>pdf</a>][<a href="/papers/v24/22-1021.bib">bib</a>]      [<a href="https://github.com/pliang279/MultiBench">code</a>] </dl> <dl> <dt>Merlion: End-to-End Machine Learning for Time Series</dt> <dd>Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu, Tian Lan, Wenzhuo Yang, Rowan Cassius, Doyen Sahoo, Devansh Arpit, Sri Subramanian, Gerald Woo, Amrita Saha, Arun Kumar Jagota, Gokulakrishnan Gopalakrishnan, Manpreet Singh, K C Krithika, Sukumar Maddineni, Daeki Cho, Bo Zong, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Steven Hoi, Huan Wang; (226):1−6, 2023. [<a href='/papers/v24/22-0809.html'>abs</a>][<a target=_blank href='/papers/volume24/22-0809/22-0809.pdf'>pdf</a>][<a href="/papers/v24/22-0809.bib">bib</a>]      [<a href="https://github.com/salesforce/Merlion">code</a>] </dl> <dl> <dt>LibMTL: A Python Library for Deep Multi-Task Learning</dt> <dd>Baijiong Lin, Yu Zhang; (209):1−7, 2023. [<a href='/papers/v24/22-0347.html'>abs</a>][<a target=_blank href='/papers/volume24/22-0347/22-0347.pdf'>pdf</a>][<a href="/papers/v24/22-0347.bib">bib</a>]      [<a href="https://github.com/median-research-group/LibMTL">code</a>] </dl> <dl> <dt>L0Learn: A Scalable Package for Sparse Learning using L0 Regularization</dt> <dd>Hussein Hazimeh, Rahul Mazumder, Tim Nonet; (205):1−8, 2023. [<a href='/papers/v24/22-0189.html'>abs</a>][<a target=_blank href='/papers/volume24/22-0189/22-0189.pdf'>pdf</a>][<a href="/papers/v24/22-0189.bib">bib</a>]      [<a href="https://github.com/hazimehh/L0Learn">code</a>] </dl> <dl> <dt>CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges</dt> <dd>Adrien Pavao, Isabelle Guyon, Anne-Catherine Letournel, Dinh-Tuan Tran, Xavier Baro, Hugo Jair Escalante, Sergio Escalera, Tyler Thomas, Zhen Xu; (198):1−6, 2023. [<a href='/papers/v24/21-1436.html'>abs</a>][<a target=_blank href='/papers/volume24/21-1436/21-1436.pdf'>pdf</a>][<a href="/papers/v24/21-1436.bib">bib</a>]      [<a href="https://github.com/codalab/codalab-competitions/">code</a>] </dl> <dl> <dt>MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning</dt> <dd>Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Yong Yu, Jun Wang, Weinan Zhang; (150):1−12, 2023. [<a href='/papers/v24/22-0169.html'>abs</a>][<a target=_blank href='/papers/volume24/22-0169/22-0169.pdf'>pdf</a>][<a href="/papers/v24/22-0169.bib">bib</a>]      [<a href="https://github.com/sjtu-marl/malib">code</a>] </dl> <dl> <dt>SQLFlow: An Extensible Toolkit Integrating DB and AI</dt> <dd>Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, ChaoChao Chen; (116):1−9, 2023. [<a href='/papers/v24/22-1047.html'>abs</a>][<a target=_blank href='/papers/volume24/22-1047/22-1047.pdf'>pdf</a>][<a href="/papers/v24/22-1047.bib">bib</a>]      [<a href="https://github.com/sql-machine-learning/sqlflow">code</a>] </dl> <dl> <dt>FedLab: A Flexible Federated Learning Framework</dt> <dd>Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu; (100):1−7, 2023. [<a href='/papers/v24/22-0440.html'>abs</a>][<a target=_blank href='/papers/volume24/22-0440/22-0440.pdf'>pdf</a>][<a href="/papers/v24/22-0440.bib">bib</a>]      [<a href="https://github.com/SMILELab-FL/FedLab">code</a>] </dl> <dl> <dt>Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond</dt> <dd>Anna Hedstr枚m, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. H枚hne; (34):1−11, 2023. [<a href='/papers/v24/22-0142.html'>abs</a>][<a target=_blank href='/papers/volume24/22-0142/22-0142.pdf'>pdf</a>][<a href="/papers/v24/22-0142.bib">bib</a>]      [<a href="https://github.com/understandable-machine-intelligence-lab/Quantus/">code</a>] </dl> <dl> <dt>HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn</dt> <dd>F谩bio M. Miranda, Niklas K枚hnecke, Bernhard Y. Renard; (29):1−17, 2023. [<a href='/papers/v24/21-1518.html'>abs</a>][<a target=_blank href='/papers/volume24/21-1518/21-1518.pdf'>pdf</a>][<a href="/papers/v24/21-1518.bib">bib</a>]      [<a href="https://github.com/scikit-learn-contrib/hiclass">code</a>] </dl> <dl> <dt>Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping</dt> <dd>XuranMeng, JeffYao; (28):1−40, 2023. [<a href='/papers/v24/21-1441.html'>abs</a>][<a target=_blank href='/papers/volume24/21-1441/21-1441.pdf'>pdf</a>][<a href="/papers/v24/21-1441.bib">bib</a>]      [<a href="https://github.com/juve-xx/watchtheweight">code</a>] </dl> <dl> <dt>Python package for causal discovery based on LiNGAM</dt> <dd>Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu; (14):1−8, 2023. [<a href='/papers/v24/21-0321.html'>abs</a>][<a target=_blank href='/papers/volume24/21-0321/21-0321.pdf'>pdf</a>][<a href="/papers/v24/21-0321.bib">bib</a>]      [<a href="https://github.com/cdt15/lingam">code</a>] </dl> <dl> <dt>AutoKeras: An AutoML Library for Deep Learning</dt> <dd>Haifeng Jin, Fran莽ois Chollet, Qingquan Song, Xia Hu; (6):1−6, 2023. [<a href='/papers/v24/20-1355.html'>abs</a>][<a target=_blank href='/papers/volume24/20-1355/20-1355.pdf'>pdf</a>][<a href="/papers/v24/20-1355.bib">bib</a>]      [<a href="https://github.com/keras-team/autokeras">code</a>] </dl> <dl> <dt>OMLT: Optimization & Machine Learning Toolkit</dt> <dd>Francesco Ceccon, Jordan Jalving, Joshua Haddad, Alexander Thebelt, Calvin Tsay, Carl D Laird, Ruth Misener; (349):1−8, 2022. [<a href='/papers/v23/22-0277.html'>abs</a>][<a target=_blank href='/papers/volume23/22-0277/22-0277.pdf'>pdf</a>][<a href="/papers/v23/22-0277.bib">bib</a>]      [<a href="https://github.com/cog-imperial/OMLT">code</a>] </dl> <dl> <dt>WarpDrive: Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU</dt> <dd>Tian Lan, Sunil Srinivasa, Huan Wang, Stephan Zheng; (316):1−6, 2022. [<a href='/papers/v23/22-0185.html'>abs</a>][<a target=_blank href='/papers/volume23/22-0185/22-0185.pdf'>pdf</a>][<a href="/papers/v23/22-0185.bib">bib</a>]      [<a href="https://github.com/salesforce/warp-drive">code</a>] </dl> <dl> <dt>d3rlpy: An Offline Deep Reinforcement Learning Library</dt> <dd>Takuma Seno, Michita Imai; (315):1−20, 2022. [<a href='/papers/v23/22-0017.html'>abs</a>][<a target=_blank href='/papers/volume23/22-0017/22-0017.pdf'>pdf</a>][<a href="/papers/v23/22-0017.bib">bib</a>]      [<a href="https://github.com/takuseno/d3rlpy">code</a>] </dl> <dl> <dt>JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data</dt> <dd>艩imon Mandl铆k, Mat臎j Ra膷insk媒, Viliam Lis媒, Tom谩拧 Pevn媒; (298):1−5, 2022. [<a href='/papers/v23/21-0174.html'>abs</a>][<a target=_blank href='/papers/volume23/21-0174/21-0174.pdf'>pdf</a>][<a href="/papers/v23/21-0174.bib">bib</a>]      [<a href="https://github.com/CTUAvastLab/JsonGrinder.jl">code</a>] </dl> <dl> <dt>ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models</dt> <dd>Francesco Martinuzzi, Chris Rackauckas, Anas Abdelrehim, Miguel D. Mahecha, Karin Mora; (288):1−8, 2022. [<a href='/papers/v23/22-0611.html'>abs</a>][<a target=_blank href='/papers/volume23/22-0611/22-0611.pdf'>pdf</a>][<a href="/papers/v23/22-0611.bib">bib</a>]      [<a href="https://github.com/SciML/ReservoirComputing.jl">code</a>] </dl> <dl> <dt>Deepchecks: A Library for Testing and Validating Machine Learning Models and Data</dt> <dd>Shir Chorev, Philip Tannor, Dan Ben Israel, Noam Bressler, Itay Gabbay, Nir Hutnik, Jonatan Liberman, Matan Perlmutter, Yurii Romanyshyn, Lior Rokach; (285):1−6, 2022. [<a href='/papers/v23/22-0281.html'>abs</a>][<a target=_blank href='/papers/volume23/22-0281/22-0281.pdf'>pdf</a>][<a href="/papers/v23/22-0281.bib">bib</a>]      [<a href="https://github.com/deepchecks/deepchecks">code</a>] </dl> <dl> <dt>CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms</dt> <dd>Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, Jo茫o G.M. Ara煤jo; (274):1−18, 2022. [<a href='/papers/v23/21-1342.html'>abs</a>][<a target=_blank href='/papers/volume23/21-1342/21-1342.pdf'>pdf</a>][<a href="/papers/v23/21-1342.bib">bib</a>]      [<a href="https://github.com/vwxyzjn/cleanrl">code</a>] </dl> <dl> <dt>Tianshou: A Highly Modularized Deep Reinforcement Learning Library</dt> <dd>Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, Jun Zhu; (267):1−6, 2022. [<a href='/papers/v23/21-1127.html'>abs</a>][<a target=_blank href='/papers/volume23/21-1127/21-1127.pdf'>pdf</a>][<a href="/papers/v23/21-1127.bib">bib</a>]      [<a href="https://github.com/thu-ml/tianshou/">code</a>] </dl> <dl> <dt>abess: A Fast Best-Subset Selection Library in Python and R</dt> <dd>Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu; (202):1−7, 2022. [<a href='/papers/v23/21-1060.html'>abs</a>][<a target=_blank href='/papers/volume23/21-1060/21-1060.pdf'>pdf</a>][<a href="/papers/v23/21-1060.bib">bib</a>]      [<a href="https://github.com/abess-team/abess">code</a>] </dl> <dl> <dt>InterpretDL: Explaining Deep Models in PaddlePaddle</dt> <dd>Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Zeyu Chen, Dejing Dou; (197):1−6, 2022. [<a href='/papers/v23/21-0738.html'>abs</a>][<a target=_blank href='/papers/volume23/21-0738/21-0738.pdf'>pdf</a>][<a href="/papers/v23/21-0738.bib">bib</a>]      [<a href="https://github.com/PaddlePaddle/InterpretDL">code</a>] </dl> <dl> <dt>ktrain: A Low-Code Library for Augmented Machine Learning</dt> <dd>Arun S. Maiya; (158):1−6, 2022. [<a href='/papers/v23/21-1124.html'>abs</a>][<a target=_blank href='/papers/volume23/21-1124/21-1124.pdf'>pdf</a>][<a href="/papers/v23/21-1124.bib">bib</a>]      [<a href="https://github.com/amaiya/ktrain">code</a>] </dl> <dl> <dt>Darts: User-Friendly Modern Machine Learning for Time Series</dt> <dd>Julien Herzen, Francesco L盲ssig, Samuele Giuliano Piazzetta, Thomas Neuer, L茅o Tafti, Guillaume Raille, Tomas Van Pottelbergh, Marek Pasieka, Andrzej Skrodzki, Nicolas Huguenin, Maxime Dumonal, Jan Ko艣cisz, Dennis Bader, Fr茅d茅rick Gusset, Mounir Benheddi, Camila Williamson, Michal Kosinski, Matej Petrik, Ga毛l Grosch; (124):1−6, 2022. [<a href='/papers/v23/21-1177.html'>abs</a>][<a target=_blank href='/papers/volume23/21-1177/21-1177.pdf'>pdf</a>][<a href="/papers/v23/21-1177.bib">bib</a>]      [<a href="https://github.com/unit8co/darts">code</a>] </dl> <dl> <dt>solo-learn: A Library of Self-supervised Methods for Visual Representation Learning</dt> <dd>Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci; (56):1−6, 2022. [<a href='/papers/v23/21-1155.html'>abs</a>][<a target=_blank href='/papers/volume23/21-1155/21-1155.pdf'>pdf</a>][<a href="/papers/v23/21-1155.bib">bib</a>]      [<a href="https://github.com/vturrisi/solo-learn">code</a>] </dl> <dl> <dt>SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization</dt> <dd>Marius Lindauer, Katharina Eggensperger, Matthias Feurer, Andr茅 Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, Ren茅 Sass, Frank Hutter; (54):1−9, 2022. [<a href='/papers/v23/21-0888.html'>abs</a>][<a target=_blank href='/papers/volume23/21-0888/21-0888.pdf'>pdf</a>][<a href="/papers/v23/21-0888.bib">bib</a>]      [<a href="https://github.com/automl/SMAC3">code</a>] </dl> <dl> <dt>DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python</dt> <dd>Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler; (53):1−6, 2022. [<a href='/papers/v23/21-0862.html'>abs</a>][<a target=_blank href='/papers/volume23/21-0862/21-0862.pdf'>pdf</a>][<a href="/papers/v23/21-0862.bib">bib</a>]      [<a href="https://github.com/DoubleML/doubleml-for-py">code</a>] </dl> <dl> <dt>Toolbox for Multimodal Learn (scikit-multimodallearn)</dt> <dd>Dominique Benielli, Baptiste Bauvin, Sokol Ko莽o, Riikka Huusari, C茅cile Capponi, Hachem Kadri, Fran莽ois Laviolette; (51):1−7, 2022. [<a href='/papers/v23/21-0791.html'>abs</a>][<a target=_blank href='/papers/volume23/21-0791/21-0791.pdf'>pdf</a>][<a href="/papers/v23/21-0791.bib">bib</a>]      [<a href="https://github.com/dbenielli/scikit-multimodallearn">code</a>] </dl> <dl> <dt>Stable-Baselines3: Reliable Reinforcement Learning Implementations</dt> <dd>Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann; (268):1−8, 2021. [<a href='/papers/v22/20-1364.html'>abs</a>][<a target=_blank href='/papers/volume22/20-1364/20-1364.pdf'>pdf</a>][<a href="/papers/v22/20-1364.bib">bib</a>]      [<a href="https://github.com/DLR-RM/stable-baselines3">code</a>] </dl> <dl> <dt>DIG: A Turnkey Library for Diving into Graph Deep Learning Research</dt> <dd>Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora M Oztekin, Xuan Zhang, Shuiwang Ji; (240):1−9, 2021. [<a href='/papers/v22/21-0343.html'>abs</a>][<a target=_blank href='/papers/volume22/21-0343/21-0343.pdf'>pdf</a>][<a href="/papers/v22/21-0343.bib">bib</a>]      [<a href="https://github.com/divelab/DIG">code</a>] </dl> <dl> <dt>sklvq: Scikit Learning Vector Quantization</dt> <dd>Rick van Veen, Michael Biehl, Gert-Jan de Vries; (231):1−6, 2021. [<a href='/papers/v22/21-0029.html'>abs</a>][<a target=_blank href='/papers/volume22/21-0029/21-0029.pdf'>pdf</a>][<a href="/papers/v22/21-0029.bib">bib</a>]      [<a href="https://github.com/rickvanveen/sklvq">code</a>] </dl> <dl> <dt>FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection</dt> <dd>Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, Qiang Yang; (226):1−6, 2021. [<a href='/papers/v22/20-815.html'>abs</a>][<a target=_blank href='/papers/volume22/20-815/20-815.pdf'>pdf</a>][<a href="/papers/v22/20-815.bib">bib</a>]      [<a href="https://github.com/FederatedAI/FATE">code</a>] </dl> <dl> <dt>TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads</dt> <dd>Pawe艂 Ro艣ciszewski, Micha艂 Martyniak, Filip Schodowski; (215):1−5, 2021. [<a href='/papers/v22/20-225.html'>abs</a>][<a target=_blank href='/papers/volume22/20-225/20-225.pdf'>pdf</a>][<a href="/papers/v22/20-225.bib">bib</a>]      [<a href="https://github.com/roscisz/TensorHive/">code</a>] </dl> <dl> <dt>dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python</dt> <dd>Hubert Baniecki, Wojciech Kretowicz, Piotr Pi膮tyszek, Jakub Wi艣niewski, Przemys艂aw Biecek; (214):1−7, 2021. [<a href='/papers/v22/20-1473.html'>abs</a>][<a target=_blank href='/papers/volume22/20-1473/20-1473.pdf'>pdf</a>][<a href="/papers/v22/20-1473.bib">bib</a>]      [<a href="https://github.com/ModelOriented/DALEX">code</a>] </dl> <dl> <dt>mlr3pipelines - Flexible Machine Learning Pipelines in R</dt> <dd>Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl; (184):1−7, 2021. [<a href='/papers/v22/21-0281.html'>abs</a>][<a target=_blank href='/papers/volume22/21-0281/21-0281.pdf'>pdf</a>][<a href="/papers/v22/21-0281.bib">bib</a>]      [<a href="https://github.com/mlr-org/mlr3pipelines">code</a>] </dl> <dl> <dt>Alibi Explain: Algorithms for Explaining Machine Learning Models</dt> <dd>Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, Alexandru Coca; (181):1−7, 2021. [<a href='/papers/v22/21-0017.html'>abs</a>][<a target=_blank href='/papers/volume22/21-0017/21-0017.pdf'>pdf</a>][<a href="/papers/v22/21-0017.bib">bib</a>]      [<a href="https://github.com/SeldonIO/alibi">code</a>] </dl> <dl> <dt>The ensmallen library for flexible numerical optimization</dt> <dd>Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou, Conrad Sanderson; (166):1−6, 2021. [<a href='/papers/v22/20-416.html'>abs</a>][<a target=_blank href='/papers/volume22/20-416/20-416.pdf'>pdf</a>][<a href="/papers/v22/20-416.bib">bib</a>]      [<a href="https://github.com/mlpack/ensmallen">code</a>] </dl> <dl> <dt>MushroomRL: Simplifying Reinforcement Learning Research</dt> <dd>Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters; (131):1−5, 2021. [<a href='/papers/v22/18-056.html'>abs</a>][<a target=_blank href='/papers/volume22/18-056/18-056.pdf'>pdf</a>][<a href="/papers/v22/18-056.bib">bib</a>]      [<a href="https://github.com/MushroomRL/mushroom-rl">code</a>] </dl> <dl> <dt>River: machine learning for streaming data in Python</dt> <dd>Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet; (110):1−8, 2021. [<a href='/papers/v22/20-1380.html'>abs</a>][<a target=_blank href='/papers/volume22/20-1380/20-1380.pdf'>pdf</a>][<a href="/papers/v22/20-1380.bib">bib</a>]      [<a href="https://github.com/online-ml/river">code</a>] </dl> <dl> <dt>mvlearn: Multiview Machine Learning in Python</dt> <dd>Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein; (109):1−7, 2021. [<a href='/papers/v22/20-1370.html'>abs</a>][<a target=_blank href='/papers/volume22/20-1370/20-1370.pdf'>pdf</a>][<a href="/papers/v22/20-1370.bib">bib</a>]      [<a href="https://github.com/mvlearn/mvlearn">code</a>] </dl> <dl> <dt>OpenML-Python: an extensible Python API for OpenML</dt> <dd>Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas M眉ller, Joaquin Vanschoren, Frank Hutter; (100):1−5, 2021. [<a href='/papers/v22/19-920.html'>abs</a>][<a target=_blank href='/papers/volume22/19-920/19-920.pdf'>pdf</a>][<a href="/papers/v22/19-920.bib">bib</a>]      [<a href="https://github.com/openml/openml-python/">code</a>] </dl> <dl> <dt>POT: Python Optimal Transport</dt> <dd>R茅mi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aur茅lie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, L茅o Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer; (78):1−8, 2021. [<a href='/papers/v22/20-451.html'>abs</a>][<a target=_blank href='/papers/volume22/20-451/20-451.pdf'>pdf</a>][<a href="/papers/v22/20-451.bib">bib</a>]      [<a href="https://github.com/PythonOT/POT">code</a>] </dl> <dl> <dt>ChainerRL: A Deep Reinforcement Learning Library</dt> <dd>Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa; (77):1−14, 2021. [<a href='/papers/v22/20-376.html'>abs</a>][<a target=_blank href='/papers/volume22/20-376/20-376.pdf'>pdf</a>][<a href="/papers/v22/20-376.bib">bib</a>]      [<a href="https://github.com/chainer/chainerrl">code</a>] </dl> <dl> <dt>Kernel Operations on the GPU, with Autodiff, without Memory Overflows</dt> <dd>Benjamin Charlier, Jean Feydy, Joan Alexis Glaun猫s, Fran莽ois-David Collin, Ghislain Durif; (74):1−6, 2021. [<a href='/papers/v22/20-275.html'>abs</a>][<a target=_blank href='/papers/volume22/20-275/20-275.pdf'>pdf</a>][<a href="/papers/v22/20-275.bib">bib</a>]      [<a href="https://github.com/getkeops/keops/">code</a>] </dl> <dl> <dt>giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data Exploration</dt> <dd>Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella P茅rez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, Kathryn Hess; (39):1−6, 2021. [<a href='/papers/v22/20-325.html'>abs</a>][<a target=_blank href='/papers/volume22/20-325/20-325.pdf'>pdf</a>][<a href="/papers/v22/20-325.bib">bib</a>]      [<a href="https://github.com/giotto-ai/giotto-tda">code</a>] </dl> <dl> <dt>Pykg2vec: A Python Library for Knowledge Graph Embedding</dt> <dd>Shih-Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque; (16):1−6, 2021. [<a href='/papers/v22/19-433.html'>abs</a>][<a target=_blank href='/papers/volume22/19-433/19-433.pdf'>pdf</a>][<a href="/papers/v22/19-433.bib">bib</a>]      [<a href="https://github.com/Sujit-O/pykg2vec">code</a>] </dl> <dl> <dt>algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD</dt> <dd>Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui; (238):1−6, 2020. [<a href='/papers/v21/19-773.html'>abs</a>][<a target=_blank href='/papers/volume21/19-773/19-773.pdf'>pdf</a>][<a href="/papers/v21/19-773.bib">bib</a>]      [<a href="https://github.com/bd2kccd/causal-compare">code</a>] </dl> <dl> <dt>Geomstats: A Python Package for Riemannian Geometry in Machine Learning</dt> <dd>Nina Miolane, Nicolas Guigui, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, Xavier Pennec; (223):1−9, 2020. [<a href='/papers/v21/19-027.html'>abs</a>][<a target=_blank href='/papers/volume21/19-027/19-027.pdf'>pdf</a>][<a href="/papers/v21/19-027.bib">bib</a>]      [<a href="https://github.com/geomstats/geomstats">code</a>] </dl> <dl> <dt>scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn</dt> <dd>Sebastian P枚lsterl; (212):1−6, 2020. [<a href='/papers/v21/20-729.html'>abs</a>][<a target=_blank href='/papers/volume21/20-729/20-729.pdf'>pdf</a>][<a href="/papers/v21/20-729.bib">bib</a>]      [<a href="https://github.com/sebp/scikit-survival">code</a>] </dl> <dl> <dt>Scikit-network: Graph Analysis in Python</dt> <dd>Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier; (185):1−6, 2020. [<a href='/papers/v21/20-412.html'>abs</a>][<a target=_blank href='/papers/volume21/20-412/20-412.pdf'>pdf</a>][<a href="/papers/v21/20-412.bib">bib</a>]      [<a href="https://github.com/sknetwork-team/scikit-network">code</a>] </dl> <dl> <dt>apricot: Submodular selection for data summarization in Python</dt> <dd>Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble; (161):1−6, 2020. [<a href='/papers/v21/19-467.html'>abs</a>][<a target=_blank href='/papers/volume21/19-467/19-467.pdf'>pdf</a>][<a href="/papers/v21/19-467.bib">bib</a>]      [<a href="https://github.com/jmschrei/apricot">code</a>] </dl> <dl> <dt>metric-learn: Metric Learning Algorithms in Python</dt> <dd>William de Vazelhes, CJ Carey, Yuan Tang, Nathalie Vauquier, Aur茅lien Bellet; (138):1−6, 2020. [<a href='/papers/v21/19-678.html'>abs</a>][<a target=_blank href='/papers/volume21/19-678/19-678.pdf'>pdf</a>][<a href="/papers/v21/19-678.bib">bib</a>]      [<a href="https://github.com/scikit-learn-contrib/metric-learn">code</a>] </dl> <dl> <dt>Probabilistic Learning on Graphs via Contextual Architectures</dt> <dd>Davide Bacciu, Federico Errica, Alessio Micheli; (134):1−39, 2020. [<a href='/papers/v21/19-470.html'>abs</a>][<a target=_blank href='/papers/volume21/19-470/19-470.pdf'>pdf</a>][<a href="/papers/v21/19-470.bib">bib</a>]      [<a href="https://github.com/diningphil/CGMM">code</a>] </dl> <dl> <dt>AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models</dt> <dd>Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovi膰, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang; (130):1−6, 2020. [<a href='/papers/v21/19-1035.html'>abs</a>][<a target=_blank href='/papers/volume21/19-1035/19-1035.pdf'>pdf</a>][<a href="/papers/v21/19-1035.bib">bib</a>]      [<a href="http://aix360.mybluemix.net">code</a>] </dl> <dl> <dt>Apache Mahout: Machine Learning on Distributed Dataflow Systems</dt> <dd>Robin Anil, Gokhan Capan, Isabel Drost-Fromm, Ted Dunning, Ellen Friedman, Trevor Grant, Shannon Quinn, Paritosh Ranjan, Sebastian Schelter, 脰zg眉r Y谋lmazel; (127):1−6, 2020. [<a href='/papers/v21/18-800.html'>abs</a>][<a target=_blank href='/papers/volume21/18-800/18-800.pdf'>pdf</a>][<a href="/papers/v21/18-800.bib">bib</a>]      [<a href="https://mahout.apache.org">code</a>] </dl> <dl> <dt>Tslearn, A Machine Learning Toolkit for Time Series Data</dt> <dd>Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, Chester Holtz, Marie Payne, Roman Yurchak, Marc Ru脽wurm, Kushal Kolar, Eli Woods; (118):1−6, 2020. [<a href='/papers/v21/20-091.html'>abs</a>][<a target=_blank href='/papers/volume21/20-091/20-091.pdf'>pdf</a>][<a href="/papers/v21/20-091.bib">bib</a>]      [<a href="https://github.com/tslearn-team/tslearn">code</a>] </dl> <dl> <dt>GluonTS: Probabilistic and Neural Time Series Modeling in Python</dt> <dd>Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner T眉rkmen, Yuyang Wang; (116):1−6, 2020. [<a href='/papers/v21/19-820.html'>abs</a>][<a target=_blank href='/papers/volume21/19-820/19-820.pdf'>pdf</a>][<a href="/papers/v21/19-820.bib">bib</a>]      [<a href="https://github.com/awslabs/gluon-ts">code</a>] </dl> <dl> <dt>MFE: Towards reproducible meta-feature extraction</dt> <dd>Edesio Alcoba莽a, Felipe Siqueira, Adriano Rivolli, Lu铆s P. F. Garcia, Jefferson T. Oliva, Andr茅 C. P. L. F. de Carvalho; (111):1−5, 2020. [<a href='/papers/v21/19-348.html'>abs</a>][<a target=_blank href='/papers/volume21/19-348/19-348.pdf'>pdf</a>][<a href="/papers/v21/19-348.bib">bib</a>]      [<a href="https://github.com/ealcobaca/pymfe">code</a>] </dl> <dl> <dt>ThunderGBM: Fast GBDTs and Random Forests on GPUs</dt> <dd>Zeyi Wen, Hanfeng Liu, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen; (108):1−5, 2020. [<a href='/papers/v21/19-095.html'>abs</a>][<a target=_blank href='/papers/volume21/19-095/19-095.pdf'>pdf</a>][<a href="/papers/v21/19-095.bib">bib</a>]      [<a href="https://github.com/xtra-computing/thundergbm">code</a>] </dl> <dl> <dt>AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings)</dt> <dd>Eugenio Bargiacchi, Diederik M. Roijers, Ann Now茅; (102):1−12, 2020. [<a href='/papers/v21/18-402.html'>abs</a>][<a target=_blank href='/papers/volume21/18-402/18-402.pdf'>pdf</a>][<a href="/papers/v21/18-402.bib">bib</a>]      [<a href="https://github.com/Svalorzen/AI-Toolbox">code</a>] </dl> <dl> <dt>pyDML: A Python Library for Distance Metric Learning</dt> <dd>Juan Luis Su谩rez, Salvador Garc铆a, Francisco Herrera; (96):1−7, 2020. [<a href='/papers/v21/19-864.html'>abs</a>][<a target=_blank href='/papers/volume21/19-864/19-864.pdf'>pdf</a>][<a href="/papers/v21/19-864.bib">bib</a>]      [<a href="https://github.com/jlsuarezdiaz/pyDML">code</a>] </dl> <dl> <dt>Cornac: A Comparative Framework for Multimodal Recommender Systems</dt> <dd>Aghiles Salah, Quoc-Tuan Truong, Hady W. Lauw; (95):1−5, 2020. [<a href='/papers/v21/19-805.html'>abs</a>][<a target=_blank href='/papers/volume21/19-805/19-805.pdf'>pdf</a>][<a href="/papers/v21/19-805.bib">bib</a>]      [<a href="https://github.com/PreferredAI/cornac">code</a>] </dl> <dl> <dt>Kymatio: Scattering Transforms in Python</dt> <dd>Mathieu Andreux, Tom谩s Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, St茅phane Mallat, Joakim And茅n, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg; (60):1−6, 2020. [<a href='/papers/v21/19-047.html'>abs</a>][<a target=_blank href='/papers/volume21/19-047/19-047.pdf'>pdf</a>][<a href="/papers/v21/19-047.bib">bib</a>]      [<a href="https://github.com/kymatio/kymatio">code</a>] </dl> <dl> <dt>GraKeL: A Graph Kernel Library in Python</dt> <dd>Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis; (54):1−5, 2020. [<a href='/papers/v21/18-370.html'>abs</a>][<a target=_blank href='/papers/volume21/18-370/18-370.pdf'>pdf</a>][<a href="/papers/v21/18-370.bib">bib</a>]      [<a href="https://github.com/ysig/GraKeL">code</a>] </dl> <dl> <dt>pyts: A Python Package for Time Series Classification</dt> <dd>Johann Faouzi, Hicham Janati; (46):1−6, 2020. [<a href='/papers/v21/19-763.html'>abs</a>][<a target=_blank href='/papers/volume21/19-763/19-763.pdf'>pdf</a>][<a href="/papers/v21/19-763.bib">bib</a>]      [<a href="https://github.com/johannfaouzi/pyts">code</a>] </dl> <dl> <dt>Tensor Train Decomposition on TensorFlow (T3F)</dt> <dd>Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan Oseledets; (30):1−7, 2020. [<a href='/papers/v21/18-008.html'>abs</a>][<a target=_blank href='/papers/volume21/18-008/18-008.pdf'>pdf</a>][<a href="/papers/v21/18-008.bib">bib</a>]      [<a href="https://github.com/Bihaqo/t3f">code</a>] </dl> <dl> <dt>ORCA: A Matlab/Octave Toolbox for Ordinal Regression</dt> <dd>Javier S谩nchez-Monedero, Pedro A. Guti茅rrez, Mar铆a P茅rez-Ortiz; (125):1−5, 2019. [<a href='/papers/v20/18-349.html'>abs</a>][<a target=_blank href='/papers/volume20/18-349/18-349.pdf'>pdf</a>][<a href="/papers/v20/18-349.bib">bib</a>]      [<a href="https://github.com/ayrna/orca">code</a>] </dl> <dl> <dt>PyOD: A Python Toolbox for Scalable Outlier Detection</dt> <dd>Yue Zhao, Zain Nasrullah, Zheng Li; (96):1−7, 2019. [<a href='/papers/v20/19-011.html'>abs</a>][<a target=_blank href='/papers/volume20/19-011/19-011.pdf'>pdf</a>][<a href="/papers/v20/19-011.bib">bib</a>]      [<a href="https://github.com/yzhao062/pyod">code</a>] </dl> <dl> <dt>iNNvestigate Neural Networks!</dt> <dd>Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam H盲gele, Kristof T. Sch眉tt, Gr茅goire Montavon, Wojciech Samek, Klaus-Robert M眉ller, Sven D盲hne, Pieter-Jan Kindermans; (93):1−8, 2019. [<a href='/papers/v20/18-540.html'>abs</a>][<a target=_blank href='/papers/volume20/18-540/18-540.pdf'>pdf</a>][<a href="/papers/v20/18-540.bib">bib</a>]      [<a href="https://github.com/albermax/innvestigate">code</a>] </dl> <dl> <dt>AffectiveTweets: a Weka Package for Analyzing Affect in Tweets</dt> <dd>Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad; (92):1−6, 2019. [<a href='/papers/v20/18-450.html'>abs</a>][<a target=_blank href='/papers/volume20/18-450/18-450.pdf'>pdf</a>][<a href="/papers/v20/18-450.bib">bib</a>]      [<a href="https://github.com/felipebravom/AffectiveTweets">code</a>] </dl> <dl> <dt>SMART: An Open Source Data Labeling Platform for Supervised Learning</dt> <dd>Rob Chew, Michael Wenger, Caroline Kery, Jason Nance, Keith Richards, Emily Hadley, Peter Baumgartner; (82):1−5, 2019. [<a href='/papers/v20/18-859.html'>abs</a>][<a target=_blank href='/papers/volume20/18-859/18-859.pdf'>pdf</a>][<a href="/papers/v20/18-859.bib">bib</a>]      [<a href="https://rtiinternational.github.io/SMART/">code</a>] </dl> <dl> <dt>Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python</dt> <dd>Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao; (44):1−5, 2019. [<a href='/papers/v20/17-722.html'>abs</a>][<a target=_blank href='/papers/volume20/17-722/17-722.pdf'>pdf</a>][<a href="/papers/v20/17-722.bib">bib</a>]      [<a href="https://github.com/jasonge27/picasso">code</a>] [<a href="https://jasonge27.github.io/picasso/">webpage</a>] </dl> <dl> <dt>Pyro: Deep Universal Probabilistic Programming</dt> <dd>Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, Noah D. Goodman; (28):1−6, 2019. [<a href='/papers/v20/18-403.html'>abs</a>][<a target=_blank href='/papers/volume20/18-403/18-403.pdf'>pdf</a>][<a href="/papers/v20/18-403.bib">bib</a>]      [<a href="https://github.com/pyro-ppl/pyro">code</a>] </dl> <dl> <dt>TensorLy: Tensor Learning in Python</dt> <dd>Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic; (26):1−6, 2019. [<a href='/papers/v20/18-277.html'>abs</a>][<a target=_blank href='/papers/volume20/18-277/18-277.pdf'>pdf</a>][<a href="/papers/v20/18-277.bib">bib</a>]      [<a href="https://github.com/tensorly/tensorly">code</a>] </dl> <dl> <dt>spark-crowd: A Spark Package for Learning from Crowdsourced Big Data</dt> <dd>Enrique G. Rodrigo, Juan A. Aledo, Jos茅 A. G谩mez; (19):1−5, 2019. [<a href='/papers/v20/17-743.html'>abs</a>][<a target=_blank href='/papers/volume20/17-743/17-743.pdf'>pdf</a>][<a href="/papers/v20/17-743.bib">bib</a>]      [<a href="https://github.com/enriquegrodrigo/spark-crowd">code</a>] </dl> <dl> <dt>scikit-multilearn: A Python library for Multi-Label Classification</dt> <dd>Piotr Szyma艅ski, Tomasz Kajdanowicz; (6):1−22, 2019. [<a href='/papers/v20/17-100.html'>abs</a>][<a target=_blank href='/papers/volume20/17-100/17-100.pdf'>pdf</a>][<a href="/papers/v20/17-100.bib">bib</a>]      [<a href="http://scikit.ml/">code</a>] </dl> <dl> <dt>Seglearn: A Python Package for Learning Sequences and Time Series</dt> <dd>David M. Burns, Cari M. Whyne; (83):1−7, 2018. [<a href='/papers/v19/18-160.html'>abs</a>][<a target=_blank href='/papers/volume19/18-160/18-160.pdf'>pdf</a>][<a href="/papers/v19/18-160.bib">bib</a>]      [<a href="https://github.com/dmbee/seglearn">code</a>] [<a href="https://dmbee.github.io/seglearn/">webpage</a>] </dl> <dl> <dt>Scikit-Multiflow: A Multi-output Streaming Framework </dt> <dd>Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem; (72):1−5, 2018. [<a href='/papers/v19/18-251.html'>abs</a>][<a target=_blank href='/papers/volume19/18-251/18-251.pdf'>pdf</a>][<a href="/papers/v19/18-251.bib">bib</a>]      [<a href="https://github.com/scikit-multiflow/scikit-multiflow">code</a>] </dl> <dl> <dt>OpenEnsembles: A Python Resource for Ensemble Clustering</dt> <dd>Tom Ronan, Shawn Anastasio, Zhijie Qi, Pedro Henrique S. Vieira Tavares, Roman Sloutsky, Kristen M. Naegle; (26):1−6, 2018. [<a href='/papers/v19/18-100.html'>abs</a>][<a target=_blank href='/papers/volume19/18-100/18-100.pdf'>pdf</a>][<a href="/papers/v19/18-100.bib">bib</a>]      [<a href="https://naeglelab.github.io/OpenEnsembles/">webpage</a>] [<a href="https://github.com/NaegleLab/OpenEnsembles">code</a>] </dl> <dl> <dt>ThunderSVM: A Fast SVM Library on GPUs and CPUs</dt> <dd>Zeyi Wen, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen; (21):1−5, 2018. [<a href='/papers/v19/17-740.html'>abs</a>][<a target=_blank href='/papers/volume19/17-740/17-740.pdf'>pdf</a>][<a href="/papers/v19/17-740.bib">bib</a>]      [<a href="https://thundersvm.readthedocs.io/en/latest/">webpage</a>] [<a href="https://github.com/Xtra-Computing/thundersvm">code</a>] </dl> <dl> <dt>ELFI: Engine for Likelihood-Free Inference</dt> <dd>Jarno Lintusaari, Henri Vuollekoski, Antti Kangasr盲盲si枚, Kusti Skyt茅n, Marko J盲rvenp盲盲, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski; (16):1−7, 2018. [<a href='/papers/v19/17-374.html'>abs</a>][<a target=_blank href='/papers/volume19/17-374/17-374.pdf'>pdf</a>][<a href="/papers/v19/17-374.bib">bib</a>]      [<a href="https://elfi.readthedocs.io">webpage</a>] [<a href="https://github.com/elfi-dev/elfi">code</a>] </dl> <dl> <dt>SGDLibrary: A MATLAB library for stochastic optimization algorithms</dt> <dd>Hiroyuki Kasai; (215):1−5, 2018. [<a href='/papers/v18/17-632.html'>abs</a>][<a target=_blank href='/papers/volume18/17-632/17-632.pdf'>pdf</a>][<a href="/papers/v18/17-632.bib">bib</a>]      [<a href="https://github.com/hiroyuki-kasai/SGDLibrary">code</a>] </dl> <dl> <dt>tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models</dt> <dd>Emmanuel Bacry, Martin Bompaire, Philip Deegan, St茅phane Ga茂ffas, S酶ren V. Poulsen; (214):1−5, 2018. [<a href='/papers/v18/17-381.html'>abs</a>][<a target=_blank href='/papers/volume18/17-381/17-381.pdf'>pdf</a>][<a href="/papers/v18/17-381.bib">bib</a>]      [<a href="https://github.com/X-DataInitiative/tick">code</a>] [<a href="https://x-datainitiative.github.io/tick/">webpage</a>] </dl> <dl> <dt>KELP: a Kernel-based Learning Platform</dt> <dd>Simone Filice, Giuseppe Castellucci, Giovanni Da San Martino, Aless, ro Moschitti, Danilo Croce, Roberto Basili; (191):1−5, 2018. [<a href='/papers/v18/16-087.html'>abs</a>][<a target=_blank href='/papers/volume18/16-087/16-087.pdf'>pdf</a>][<a href="/papers/v18/16-087.bib">bib</a>]      [<a href="https://github.com/SAG-KeLP">code</a>] [<a href="http://www.kelp-ml.org">webpage</a>] </dl> <dl> <dt>Pycobra: A Python Toolbox for Ensemble Learning and Visualisation</dt> <dd>Benjamin Guedj, Bhargav Srinivasa Desikan; (190):1−5, 2018. [<a href='/papers/v18/17-228.html'>abs</a>][<a target=_blank href='/papers/volume18/17-228/17-228.pdf'>pdf</a>][<a href="/papers/v18/17-228.bib">bib</a>]      [<a href="https://github.com/bhargavvader/pycobra">code</a>] [<a href="https://modal.lille.inria.fr/pycobra/">webpage</a>] </dl> <dl> <dt>HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data</dt> <dd>Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning; (152):1−6, 2018. [<a href='/papers/v18/17-434.html'>abs</a>][<a target=_blank href='/papers/volume18/17-434/17-434.pdf'>pdf</a>][<a href="/papers/v18/17-434.bib">bib</a>]      [<a href="https://github.com/ContextLab/hypertools">code</a>] [<a href="https://hypertools.readthedocs.io/en/latest/">webpage</a>] </dl> <dl> <dt>openXBOW -- Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit</dt> <dd>Maximilian Schmitt, Bj枚rn Schuller; (96):1−5, 2017. [<a href='/papers/v18/17-113.html'>abs</a>][<a target=_blank href='/papers/volume18/17-113/17-113.pdf'>pdf</a>][<a href="/papers/v18/17-113.bib">bib</a>]      [<a href="https://github.com/openXBOW/openXBOW">code</a>] </dl> <dl> <dt>The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems</dt> <dd>Frans A. Oliehoek, Matthijs T. J. Spaan, Bas Terwijn, Philipp Robbel, Jo\~{a}o V. Messias; (89):1−5, 2017. [<a href='/papers/v18/17-156.html'>abs</a>][<a target=_blank href='/papers/volume18/17-156/17-156.pdf'>pdf</a>][<a href="/papers/v18/17-156.bib">bib</a>]      [<a href="https://github.com/MADPToolbox/MADP">code</a>] </dl> <dl> <dt>GPflow: A Gaussian Process Library using TensorFlow</dt> <dd>Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo Le{\'o}n-Villagr{\'a}, Zoubin Ghahramani, James Hensman; (40):1−6, 2017. [<a href='/papers/v18/16-537.html'>abs</a>][<a target=_blank href='/papers/volume18/16-537/16-537.pdf'>pdf</a>][<a href="/papers/v18/16-537.bib">bib</a>]      [<a href="https://github.com/GPflow/GPflow">code</a>] [<a href="https://www.gpflow.org">webpage</a>] </dl> <dl> <dt>GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis</dt> <dd>Eemeli Lepp盲aho, Muhammad Ammad-ud-din, Samuel Kaski; (39):1−5, 2017. [<a href='/papers/v18/16-509.html'>abs</a>][<a target=_blank href='/papers/volume18/16-509/16-509.pdf'>pdf</a>][<a href="/papers/v18/16-509.bib">bib</a>]      [<a href="https://cran.r-project.org/web/packages/GFA/index.html">code</a>] [<a href="https://cran.r-project.org/web/packages/GFA/index.html">r-project.org</a>] </dl> <dl> <dt>POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty</dt> <dd>Maxim Egorov, Zachary N. Sunberg, Edward Balaban, Tim A. Wheeler, Jayesh K. Gupta, Mykel J. Kochenderfer; (26):1−5, 2017. [<a href='/papers/v18/16-300.html'>abs</a>][<a target=_blank href='/papers/volume18/16-300/16-300.pdf'>pdf</a>][<a href="/papers/v18/16-300.bib">bib</a>]      [<a href="https://github.com/JuliaPOMDP/POMDPs.jl">code</a>] [<a href="http://juliapomdp.github.io/">webpage</a>] </dl> <dl> <dt>Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA</dt> <dd>Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown; (25):1−5, 2017. [<a href='/papers/v18/16-261.html'>abs</a>][<a target=_blank href='/papers/volume18/16-261/16-261.pdf'>pdf</a>][<a href="/papers/v18/16-261.bib">bib</a>]      [<a href="https://github.com/automl/autoweka">code</a>] [<a href="http://www.cs.ubc.ca/labs/beta/Projects/autoweka/">webpage</a>] </dl> <dl> <dt>JSAT: Java Statistical Analysis Tool, a Library for Machine Learning</dt> <dd>Edward Raff; (23):1−5, 2017. [<a href='/papers/v18/16-131.html'>abs</a>][<a target=_blank href='/papers/volume18/16-131/16-131.pdf'>pdf</a>][<a href="/papers/v18/16-131.bib">bib</a>]      [<a href="https://github.com/EdwardRaff/JSAT">code</a>] [<a href="https://github.com/EdwardRaff/JSAT">webpage</a>] </dl> <dl> <dt>Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning</dt> <dd>Guillaume Lema卯tre, Fernando Nogueira, Christos K. Aridas; (17):1−5, 2017. [<a href='/papers/v18/16-365.html'>abs</a>][<a target=_blank href='/papers/volume18/16-365/16-365.pdf'>pdf</a>][<a href="/papers/v18/16-365.bib">bib</a>]      [<a href="https://github.com/scikit-learn-contrib/imbalanced-learn">code</a>] [<a href="http://contrib.scikit-learn.org/imbalanced-learn/">webpage</a>] </dl> <dl> <dt>Refinery: An Open Source Topic Modeling Web Platform</dt> <dd>Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth; (12):1−5, 2017. [<a href='/papers/v18/15-441.html'>abs</a>][<a target=_blank href='/papers/volume18/15-441/15-441.pdf'>pdf</a>][<a href="/papers/v18/15-441.bib">bib</a>]      [<a href="https://github.com/daeilkim/refinery">code</a>] [<a href="http://daeilkim.github.io/refinery/">webpage</a>] </dl> <dl> <dt>SnapVX: A Network-Based Convex Optimization Solver</dt> <dd>David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosi膷, Stephen Boyd, Jure Leskovec; (4):1−5, 2017. [<a href='/papers/v18/15-492.html'>abs</a>][<a target=_blank href='/papers/volume18/15-492/15-492.pdf'>pdf</a>][<a href="/papers/v18/15-492.bib">bib</a>]      [<a href="http://snap.stanford.edu/snapvx/#install">code</a>] [<a href="http://snap.stanford.edu/snapvx/">stanford.edu</a>] </dl> <dl> <dt>fastFM: A Library for Factorization Machines</dt> <dd>Immanuel Bayer; (184):1−5, 2016. [<a href='/papers/v17/15-355.html'>abs</a>][<a target=_blank href='/papers/volume17/15-355/15-355.pdf'>pdf</a>][<a href="/papers/v17/15-355.bib">bib</a>]      [<a href="https://github.com/ibayer/fastFM">code</a>] [<a href="http://ibayer.github.io/fastFM/">webpage</a>] </dl> <dl> <dt>Megaman: Scalable Manifold Learning in Python</dt> <dd>James McQueen, Marina Meil膬, Jacob VanderPlas, Zhongyue Zhang; (148):1−5, 2016. [<a href='/papers/v17/16-109.html'>abs</a>][<a target=_blank href='/papers/volume17/16-109/16-109.pdf'>pdf</a>][<a href="/papers/v17/16-109.bib">bib</a>]      [<a href="https://github.com/mmp2/megaman">code</a>] [<a href="http://mmp2.github.io/megaman/">webpage</a>] </dl> <dl> <dt>JCLAL: A Java Framework for Active Learning</dt> <dd>Oscar Reyes, Eduardo P茅rez, Mar铆a del Carmen Rodr铆guez-Hern谩ndez, Habib M. Fardoun, Sebasti谩n Ventura; (95):1−5, 2016. [<a href='/papers/v17/15-347.html'>abs</a>][<a target=_blank href='/papers/volume17/15-347/15-347.pdf'>pdf</a>][<a href="/papers/v17/15-347.bib">bib</a>]      [<a href="https://sourceforge.net/projects/jclal/">code</a>] </dl> <dl> <dt>LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems</dt> <dd>Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin; (86):1−5, 2016. [<a href='/papers/v17/15-471.html'>abs</a>][<a target=_blank href='/papers/volume17/15-471/15-471.pdf'>pdf</a>][<a href="/papers/v17/15-471.bib">bib</a>]      [<a href="https://www.csie.ntu.edu.tw/~cjlin/libmf/">code</a>] </dl> <dl> <dt>CVXPY: A Python-Embedded Modeling Language for Convex Optimization</dt> <dd>Steven Diamond, Stephen Boyd; (83):1−5, 2016. [<a href='/papers/v17/15-408.html'>abs</a>][<a target=_blank href='/papers/volume17/15-408/15-408.pdf'>pdf</a>][<a href="/papers/v17/15-408.bib">bib</a>]      [<a href="https://github.com/cvxgrp/cvxpy">code</a>] [<a href="https://www.cvxpy.org">webpage</a>] </dl> <dl> <dt>Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches</dt> <dd>Jure 沤bontar, Yann LeCun; (65):1−32, 2016. [<a href='/papers/v17/15-535.html'>abs</a>][<a target=_blank href='/papers/volume17/15-535/15-535.pdf'>pdf</a>][<a href="/papers/v17/15-535.bib">bib</a>]      [<a href="https://github.com/jzbontar/mc-cnn">code</a>] </dl> <dl> <dt>MLlib: Machine Learning in Apache Spark</dt> <dd>Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar; (34):1−7, 2016. [<a href='/papers/v17/15-237.html'>abs</a>][<a target=_blank href='/papers/volume17/15-237/15-237.pdf'>pdf</a>][<a href="/papers/v17/15-237.bib">bib</a>]      [<a href="http://spark.apache.org/downloads.html">code</a>] [<a href="http://spark.apache.org/mllib/">webpage</a>] </dl> <dl> <dt>MEKA: A Multi-label/Multi-target Extension to WEKA</dt> <dd>Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes; (21):1−5, 2016. [<a href='/papers/v17/12-164.html'>abs</a>][<a target=_blank href='/papers/volume17/12-164/12-164.pdf'>pdf</a>][<a href="/papers/v17/12-164.bib">bib</a>]      [<a href="https://github.com/Waikato/meka">code</a>] [<a href="http://waikato.github.io/meka/">webpage</a>] </dl> <dl> <dt>Harry: A Tool for Measuring String Similarity</dt> <dd>Konrad Rieck, Christian Wressnegger; (9):1−5, 2016. [<a href='/papers/v17/rieck16a.html'>abs</a>][<a target=_blank href='/papers/volume17/rieck16a/rieck16a.pdf'>pdf</a>][<a href="/papers/v17/rieck16a.bib">bib</a>]      [<a href="https://github.com/rieck/harry">code</a>] [<a href="http://mlsec.org/harry/">webpage</a>] </dl> <dl> <dt>partykit: A Modular Toolkit for Recursive Partytioning in R</dt> <dd>Torsten Hothorn, Achim Zeileis; (118):3905−3909, 2015. [<a href='/papers/v16/hothorn15a.html'>abs</a>][<a target=_blank href='/papers/volume16/hothorn15a/hothorn15a.pdf'>pdf</a>][<a href="/papers/v16/hothorn15a.bib">bib</a>]      [<a href="https://cran.r-project.org/web/packages/partykit/index.html">code</a>] </dl> <dl> <dt>CEKA: A Tool for Mining the Wisdom of Crowds</dt> <dd>Jing Zhang, Victor S. Sheng, Bryce A. Nicholson, Xindong Wu; (88):2853−2858, 2015. [<a href='/papers/v16/zhang15a.html'>abs</a>][<a target=_blank href='/papers/volume16/zhang15a/zhang15a.pdf'>pdf</a>][<a href="/papers/v16/zhang15a.bib">bib</a>]      [<a href="http://ceka.sourceforge.net">code</a>] </dl> <dl> <dt>pyGPs -- A Python Library for Gaussian Process Regression and Classification</dt> <dd>Marion Neumann, Shan Huang, Daniel E. Marthaler, Kristian Kersting; (80):2611−2616, 2015. [<a href='/papers/v16/neumann15a.html'>abs</a>][<a target=_blank href='/papers/volume16/neumann15a/neumann15a.pdf'>pdf</a>][<a href="/papers/v16/neumann15a.bib">bib</a>]      [<a href="https://github.com/PMBio/pygp">code</a>] </dl> <dl> <dt>The Libra Toolkit for Probabilistic Models</dt> <dd>Daniel Lowd, Amirmohammad Rooshenas; (75):2459−2463, 2015. [<a href='/papers/v16/lowd15a.html'>abs</a>][<a target=_blank href='/papers/volume16/lowd15a/lowd15a.pdf'>pdf</a>][<a href="/papers/v16/lowd15a.bib">bib</a>]      [<a href="http://libra.cs.uoregon.edu">code</a>] </dl> <dl> <dt>RLPy: A Value-Function-Based Reinforcement Learning Framework for Education and Research</dt> <dd>Alborz Geramifard, Christoph Dann, Robert H. Klein, William Dabney, Jonathan P. How; (46):1573−1578, 2015. [<a href='/papers/v16/geramifard15a.html'>abs</a>][<a target=_blank href='/papers/volume16/geramifard15a/geramifard15a.pdf'>pdf</a>][<a href="/papers/v16/geramifard15a.bib">bib</a>]      [<a href="https://github.com/rlpy/rlpy/">code</a>] </dl> <dl> <dt>Encog: Library of Interchangeable Machine Learning Models for Java and C#</dt> <dd>Jeff Heaton; (36):1243−1247, 2015. [<a href='/papers/v16/heaton15a.html'>abs</a>][<a target=_blank href='/papers/volume16/heaton15a/heaton15a.pdf'>pdf</a>][<a href="/papers/v16/heaton15a.bib">bib</a>]      [<a href="http://www.heatonresearch.com/download/">code</a>] [<a href="http://www.encog.org/">webpage</a>] </dl> <dl> <dt>The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R</dt> <dd>Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu; (18):553−557, 2015. [<a href='/papers/v16/li15a.html'>abs</a>][<a target=_blank href='/papers/volume16/li15a/li15a.pdf'>pdf</a>][<a href="/papers/v16/li15a.bib">bib</a>]      [<a href="http://cran.r-project.org/src/contrib/flare_1.5.0.tar.gz">code</a>] [<a href="http://cran.r-project.org/web/packages/flare/index.html">webpage</a>] </dl> <dl> <dt>Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit</dt> <dd>Felix Weninger; (17):547−551, 2015. [<a href='/papers/v16/weninger15a.html'>abs</a>][<a target=_blank href='/papers/volume16/weninger15a/weninger15a.pdf'>pdf</a>][<a href="/papers/v16/weninger15a.bib">bib</a>]      [<a href="http://sourceforge.net/projects/currennt/">code</a>] </dl> <dl> <dt>A Classification Module for Genetic Programming Algorithms in JCLEC</dt> <dd>Alberto Cano, Jos茅 Mar铆a Luna, Amelia Zafra, Sebasti谩n Ventura; (15):491−494, 2015. [<a href='/papers/v16/cano15a.html'>abs</a>][<a target=_blank href='/papers/volume16/cano15a/cano15a.pdf'>pdf</a>][<a href="/papers/v16/cano15a.bib">bib</a>]      [<a href="http://samoa.incubator.apache.org">code</a>] </dl> <dl> <dt>SAMOA: Scalable Advanced Massive Online Analysis</dt> <dd>Gianmarco De Francisci Morales, Albert Bifet; (5):149−153, 2015. [<a href='/papers/v16/morales15a.html'>abs</a>][<a target=_blank href='/papers/volume16/morales15a/morales15a.pdf'>pdf</a>][<a href="/papers/v16/morales15a.bib">bib</a>]      [<a href="http://jclec.sourceforge.net">code</a>] </dl> <dl> <dt>BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits</dt> <dd>Ruben Martinez-Cantin; (115):3915−3919, 2014. [<a href='/papers/v15/martinezcantin14a.html'>abs</a>][<a target=_blank href='/papers/volume15/martinezcantin14a/martinezcantin14a.pdf'>pdf</a>][<a href="/papers/v15/martinezcantin14a.bib">bib</a>]      [<a href="https://github.com/rmcantin/bayesopt">code</a>] </dl> <dl> <dt>SPMF: A Java Open-Source Pattern Mining Library</dt> <dd>Philippe Fournier-Viger, Antonio Gomariz, Ted Gueniche, Azadeh Soltani, Cheng-Wei Wu, Vincent S. Tseng; (104):3569−3573, 2014. [<a href='/papers/v15/fournierviger14a.html'>abs</a>][<a target=_blank href='/papers/volume15/fournierviger14a/fournierviger14a.pdf'>pdf</a>][<a href="/papers/v15/fournierviger14a.bib">bib</a>]      [<a href="https://www.philippe-fournier-viger.com/spmf/">code</a>] </dl> <dl> <dt>The Gesture Recognition Toolkit</dt> <dd>Nicholas Gillian, Joseph A. Paradiso; (101):3483−3487, 2014. [<a href='/papers/v15/gillian14a.html'>abs</a>][<a target=_blank href='/papers/volume15/gillian14a/gillian14a.pdf'>pdf</a>][<a href="/papers/v15/gillian14a.bib">bib</a>]      [<a href="https://github.com/nickgillian/grt">code</a>] </dl> <dl> <dt>ooDACE Toolbox: A Flexible Object-Oriented Kriging Implementation</dt> <dd>Ivo Couckuyt, Tom Dhaene, Piet Demeester; (91):3183−3186, 2014. [<a href='/papers/v15/couckuyt14a.html'>abs</a>][<a target=_blank href='/papers/volume15/couckuyt14a/couckuyt14a.pdf'>pdf</a>][<a href="/papers/v15/couckuyt14a.bib">bib</a>]      [<a href="https://cran.r-project.org/web/packages/DiceKriging/index.html">code</a>] </dl> <dl> <dt>pystruct - Learning Structured Prediction in Python</dt> <dd>Andreas C. M眉ller, Sven Behnke; (59):2055−2060, 2014. [<a href='/papers/v15/mueller14a.html'>abs</a>][<a target=_blank href='/papers/volume15/mueller14a/mueller14a.pdf'>pdf</a>][<a href="/papers/v15/mueller14a.bib">bib</a>]      [<a href="https://pystruct.github.io">code</a>] </dl> <dl> <dt>Manopt, a Matlab Toolbox for Optimization on Manifolds</dt> <dd>Nicolas Boumal, Bamdev Mishra, P.-A. Absil, Rodolphe Sepulchre; (42):1455−1459, 2014. [<a href='/papers/v15/boumal14a.html'>abs</a>][<a target=_blank href='/papers/volume15/boumal14a/boumal14a.pdf'>pdf</a>][<a href="/papers/v15/boumal14a.bib">bib</a>]      [<a href="https://www.manopt.org">code</a>] </dl> <dl> <dt>Conditional Random Field with High-order Dependencies for Sequence Labeling and Segmentation</dt> <dd>Nguyen Viet Cuong, Nan Ye, Wee Sun Lee, Hai Leong Chieu; (28):981−1009, 2014. [<a href='/papers/v15/cuong14a.html'>abs</a>][<a target=_blank href='/papers/volume15/cuong14a/cuong14a.pdf'>pdf</a>][<a href="/papers/v15/cuong14a.bib">bib</a>]      [<a href="https://github.com/nvcuong/HOSemiCRF">code</a>] </dl> <dl> <dt>LIBOL: A Library for Online Learning Algorithms</dt> <dd>Steven C.H. Hoi, Jialei Wang, Peilin Zhao; (15):495−499, 2014. [<a href='/papers/v15/hoi14a.html'>abs</a>][<a target=_blank href='/papers/volume15/hoi14a/hoi14a.pdf'>pdf</a>][<a href="/papers/v15/hoi14a.bib">bib</a>]      [<a href="https://github.com/LIBOL/LIBOL">code</a>] </dl> <dl> <dt>The FASTCLIME Package for Linear Programming and Large-Scale Precision Matrix Estimation in R</dt> <dd>Haotian Pang, Han Liu, Robert V, erbei; (14):489−493, 2014. [<a href='/papers/v15/pang14a.html'>abs</a>][<a target=_blank href='/papers/volume15/pang14a/pang14a.pdf'>pdf</a>][<a href="/papers/v15/pang14a.bib">bib</a>]      [<a href="http://cran.nexr.com/web/packages/fastclime/">code</a>] </dl> <dl> <dt>Information Theoretical Estimators Toolbox</dt> <dd>Zolt谩n Szab贸; (9):283−287, 2014. [<a href='/papers/v15/szabo14a.html'>abs</a>][<a target=_blank href='/papers/volume15/szabo14a/szabo14a.pdf'>pdf</a>][<a href="/papers/v15/szabo14a.bib">bib</a>]      [<a href="https://bitbucket.org/szzoli/ite/">code</a>] </dl> <dl> <dt>EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines</dt> <dd>Marc Claesen, Frank De Smet, Johan A.K. Suykens, Bart De Moor; (4):141−145, 2014. [<a href='/papers/v15/claesen14a.html'>abs</a>][<a target=_blank href='/papers/volume15/claesen14a/claesen14a.pdf'>pdf</a>][<a href="/papers/v15/claesen14a.bib">bib</a>]      [<a href="https://github.com/claesenm/EnsembleSVM">code</a>] </dl> <dl> <dt>GURLS: A Least Squares Library for Supervised Learning</dt> <dd>Andrea Tacchetti, Pavan K. Mallapragada, Matteo Santoro, Lorenzo Rosasco; (100):3201−3205, 2013. [<a href='/papers/v14/tacchetti13a.html'>abs</a>][<a target=_blank href='/papers/volume14/tacchetti13a/tacchetti13a.pdf'>pdf</a>][<a href="/papers/v14/tacchetti13a.bib">bib</a>]      [<a href="https://github.com/LCSL/GURLS">code</a>] </dl> <dl> <dt>Divvy: Fast and Intuitive Exploratory Data Analysis</dt> <dd>Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten; (98):3159−3163, 2013. [<a href='/papers/v14/lewis13a.html'>abs</a>][<a target=_blank href='/papers/volume14/lewis13a/lewis13a.pdf'>pdf</a>][<a href="/papers/v14/lewis13a.bib">bib</a>]      [<a href="https://github.com/jmlewis/divvy">code</a>] [<a href="https://divvy.ucsd.edu/">webpage</a>] </dl> <dl> <dt>QuantMiner for Mining Quantitative Association Rules</dt> <dd>Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, Daniel Cassard; (97):3153−3157, 2013. [<a href='/papers/v14/salleb-aouissi13a.html'>abs</a>][<a target=_blank href='/papers/volume14/salleb-aouissi13a/salleb-aouissi13a.pdf'>pdf</a>][<a href="/papers/v14/salleb-aouissi13a.bib">bib</a>]      [<a href="https://github.com/QuantMiner/QuantMiner">code</a>] </dl> <dl> <dt>The CAM Software for Nonnegative Blind Source Separation in R-Java</dt> <dd>Niya Wang, Fan Meng, Li Chen, Subha Madhavan, Robert Clarke, Eric P. Hoffman, Jianhua Xuan, Yue Wang; (88):2899−2903, 2013. [<a href='/papers/v14/wang13d.html'>abs</a>][<a target=_blank href='/papers/volume14/wang13d/wang13d.pdf'>pdf</a>][<a href="/papers/v14/wang13d.bib">bib</a>]      [<a href="https://mloss.org/software/view/437/">code</a>] </dl> <dl> <dt>BudgetedSVM: A Toolbox for Scalable SVM Approximations</dt> <dd>Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang; (84):3813−3817, 2013. [<a href='/papers/v14/djuric13a.html'>abs</a>][<a target=_blank href='/papers/volume14/djuric13a/djuric13a.pdf'>pdf</a>][<a href="/papers/v14/djuric13a.bib">bib</a>]      [<a href="https://github.com/djurikom/BudgetedSVM">code</a>] </dl> <dl> <dt>Tapkee: An Efficient Dimension Reduction Library</dt> <dd>Sergey Lisitsyn, Christian Widmer, Fernando J. Iglesias Garcia; (72):2355−2359, 2013. [<a href='/papers/v14/lisitsyn13a.html'>abs</a>][<a target=_blank href='/papers/volume14/lisitsyn13a/lisitsyn13a.pdf'>pdf</a>][<a href="/papers/v14/lisitsyn13a.bib">bib</a>]      [<a href="https://github.com/lisitsyn/tapkee">code</a>] </dl> <dl> <dt>Orange: Data Mining Toolbox in Python</dt> <dd>Janez Dem拧ar, Toma啪 Curk, Ale拧 Erjavec, 膶rt Gorup, Toma啪 Ho膷evar, Mitar Milutinovi膷, Martin Mo啪ina, Matija Polajnar, Marko Toplak, An啪e Stari膷, Miha 艩tajdohar, Lan Umek, Lan 沤agar, Jure 沤bontar, Marinka 沤itnik, Bla啪 Zupan; (71):2349−2353, 2013. [<a href='/papers/v14/demsar13a.html'>abs</a>][<a target=_blank href='/papers/volume14/demsar13a/demsar13a.pdf'>pdf</a>][<a href="/papers/v14/demsar13a.bib">bib</a>]      [<a href="https://github.com/biolab/orange3">code</a>] </dl> <dl> <dt>JKernelMachines: A Simple Framework for Kernel Machines</dt> <dd>David Picard, Nicolas Thome, Matthieu Cord; (43):1417−1421, 2013. [<a href='/papers/v14/picard13a.html'>abs</a>][<a target=_blank href='/papers/volume14/picard13a/picard13a.pdf'>pdf</a>][<a href="/papers/v14/picard13a.bib">bib</a>]      [<a href="https://github.com/davidpicard/jkernelmachines">code</a>] </dl> <dl> <dt>GPstuff: Bayesian Modeling with Gaussian Processes</dt> <dd>Jarno Vanhatalo, Jaakko Riihim盲ki, Jouni Hartikainen, Pasi Jyl盲nki, Ville Tolvanen, Aki Vehtari; (35):1175−1179, 2013. [<a href='/papers/v14/vanhatalo13a.html'>abs</a>][<a target=_blank href='/papers/volume14/vanhatalo13a/vanhatalo13a.pdf'>pdf</a>][<a href="/papers/v14/vanhatalo13a.bib">bib</a>]      [<a href="https://github.com/gpstuff-dev/gpstuff">code</a>] </dl> <dl> <dt>MLPACK: A Scalable C++ Machine Learning Library</dt> <dd>Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram, Nishant A. Mehta, Alexander G. Gray; (24):801−805, 2013. [<a href='/papers/v14/curtin13a.html'>abs</a>][<a target=_blank href='/papers/volume14/curtin13a/curtin13a.pdf'>pdf</a>][<a href="/papers/v14/curtin13a.bib">bib</a>]      [<a href="https://github.com/mlpack/mlpack">code</a>] </dl> <dl> <dt>A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics</dt> <dd>Herv茅 Frezza-Buet, Matthieu Geist; (18):625−628, 2013. [<a href='/papers/v14/frezza-buet13a.html'>abs</a>][<a target=_blank href='/papers/volume14/frezza-buet13a/frezza-buet13a.pdf'>pdf</a>][<a href="/papers/v14/frezza-buet13a.bib">bib</a>]      [<a href="https://github.com/HerveFrezza-Buet/RLlib">code</a>] </dl> <dl> <dt>SVDFeature: A Toolkit for Feature-based Collaborative Filtering</dt> <dd>Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu; (116):3619−3622, 2012. [<a href='/papers/v13/chen12a.html'>abs</a>][<a target=_blank href='/papers/volume13/chen12a/chen12a.pdf'>pdf</a>][<a href="/papers/v13/chen12a.bib">bib</a>]      [<a href="https://mloss.org/software/view/333/">code</a>] </dl> <dl> <dt>DARWIN: A Framework for Machine Learning and Computer Vision Research and Development</dt> <dd>Stephen Gould; (113):3533−3537, 2012. [<a href='/papers/v13/gould12a.html'>abs</a>][<a target=_blank href='/papers/volume13/gould12a/gould12a.pdf'>pdf</a>][<a href="/papers/v13/gould12a.bib">bib</a>]      [<a href="https://github.com/sgould/drwn">code</a>] </dl> <dl> <dt>Sally: A Tool for Embedding Strings in Vector Spaces</dt> <dd>Konrad Rieck, Christian Wressnegger, Alexander Bikadorov; (104):3247−3251, 2012. [<a href='/papers/v13/rieck12a.html'>abs</a>][<a target=_blank href='/papers/volume13/rieck12a/rieck12a.pdf'>pdf</a>][<a href="/papers/v13/rieck12a.bib">bib</a>]      [<a href="https://github.com/rieck/sally">code</a>] </dl> <dl> <dt>Oger: Modular Learning Architectures For Large-Scale Sequential Processing</dt> <dd>David Verstraeten, Benjamin Schrauwen, Sander Dieleman, Philemon Brakel, Pieter Buteneers, Dejan Pecevski; (96):2995−2998, 2012. [<a href='/papers/v13/verstraeten12a.html'>abs</a>][<a target=_blank href='/papers/volume13/verstraeten12a/verstraeten12a.pdf'>pdf</a>][<a href="/papers/v13/verstraeten12a.bib">bib</a>]      [<a href="http://organic.elis.ugent.be/oger">code</a>] </dl> <dl> <dt>PREA: Personalized Recommendation Algorithms Toolkit</dt> <dd>Joonseok Lee, Mingxuan Sun, Guy Lebanon; (87):2699−2703, 2012. [<a href='/papers/v13/lee12b.html'>abs</a>][<a target=_blank href='/papers/volume13/lee12b/lee12b.pdf'>pdf</a>][<a href="/papers/v13/lee12b.bib">bib</a>]      [<a href="https://mloss.org/software/view/420/">code</a>] </dl> <dl> <dt>A Topic Modeling Toolbox Using Belief Propagation</dt> <dd>Jia Zeng; (73):2233−2236, 2012. [<a href='/papers/v13/zeng12a.html'>abs</a>][<a target=_blank href='/papers/volume13/zeng12a/zeng12a.pdf'>pdf</a>][<a href="/papers/v13/zeng12a.bib">bib</a>]      [<a href="https://mloss.org/software/view/399/">code</a>] </dl> <dl> <dt>DEAP: Evolutionary Algorithms Made Easy</dt> <dd>F茅lix-Antoine Fortin, Fran莽ois-Michel De Rainville, Marc-Andr茅 Gardner, Marc Parizeau, Christian Gagn茅; (70):2171−2175, 2012. [<a href='/papers/v13/fortin12a.html'>abs</a>][<a target=_blank href='/papers/volume13/fortin12a/fortin12a.pdf'>pdf</a>][<a href="/papers/v13/fortin12a.bib">bib</a>]      [<a href="https://github.com/DEAP/deap">code</a>] </dl> <dl> <dt>Pattern for Python</dt> <dd>Tom De Smedt, Walter Daelemans; (66):2063−2067, 2012. [<a href='/papers/v13/desmedt12a.html'>abs</a>][<a target=_blank href='/papers/volume13/desmedt12a/desmedt12a.pdf'>pdf</a>][<a href="/papers/v13/desmedt12a.bib">bib</a>]      [<a href="http://www.clips.ua.ac.be/pages/pattern">code</a>] </dl> <dl> <dt>Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences</dt> <dd>Jan Grau, Jens Keilwagen, Andr茅 Gohr, Berit Haldemann, Stefan Posch, Ivo Grosse; (62):1967−1971, 2012. [<a href='/papers/v13/grau12a.html'>abs</a>][<a target=_blank href='/papers/volume13/grau12a/grau12a.pdf'>pdf</a>][<a href="/papers/v13/grau12a.bib">bib</a>]      [<a href="http://www.jstacs.de">code</a>] </dl> <dl> <dt>glm-ie: Generalised Linear Models Inference & Estimation Toolbox</dt> <dd>Hannes Nickisch; (54):1699−1703, 2012. [<a href='/papers/v13/nickisch12a.html'>abs</a>][<a target=_blank href='/papers/volume13/nickisch12a/nickisch12a.pdf'>pdf</a>][<a href="/papers/v13/nickisch12a.bib">bib</a>]      [<a href="https://mloss.org/software/view/269/">code</a>] </dl> <dl> <dt>The huge Package for High-dimensional Undirected Graph Estimation in R</dt> <dd>Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman; (37):1059−1062, 2012. [<a href='/papers/v13/zhao12a.html'>abs</a>][<a target=_blank href='/papers/volume13/zhao12a/zhao12a.pdf'>pdf</a>][<a href="/papers/v13/zhao12a.bib">bib</a>]      [<a href="https://cran.r-project.org/web/packages/huge/">code</a>] </dl> <dl> <dt>NIMFA : A Python Library for Nonnegative Matrix Factorization</dt> <dd>Marinka 沤itnik, Bla啪 Zupan; (30):849−853, 2012. [<a href='/papers/v13/zitnik12a.html'>abs</a>][<a target=_blank href='/papers/volume13/zitnik12a/zitnik12a.pdf'>pdf</a>][<a href="/papers/v13/zitnik12a.bib">bib</a>]      [<a href="https://github.com/mims-harvard/nimfa">code</a>] </dl> <dl> <dt>GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression</dt> <dd>Chiwoo Park, Jianhua Z. Huang, Yu Ding; (26):775−779, 2012. [<a href='/papers/v13/park12a.html'>abs</a>][<a target=_blank href='/papers/volume13/park12a/park12a.pdf'>pdf</a>][<a href="/papers/v13/park12a.bib">bib</a>]      [<a href="http://mloss.org/revision/download/990/">code</a>] </dl> <dl> <dt>ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel</dt> <dd>Stephen R. Piccolo, Lewis J. Frey; (19):555−559, 2012. [<a href='/papers/v13/piccolo12a.html'>abs</a>][<a target=_blank href='/papers/volume13/piccolo12a/piccolo12a.pdf'>pdf</a>][<a href="/papers/v13/piccolo12a.bib">bib</a>]      [<a href="http://mlflex.sourceforge.net">code</a>] </dl> <dl> <dt>MULTIBOOST: A Multi-purpose Boosting Package</dt> <dd>Djalel Benbouzid, R贸bert Busa-Fekete, Norman Casagrande, Fran莽ois-David Collin, Bal谩zs K茅gl; (18):549−553, 2012. [<a href='/papers/v13/benbouzid12a.html'>abs</a>][<a target=_blank href='/papers/volume13/benbouzid12a/benbouzid12a.pdf'>pdf</a>][<a href="/papers/v13/benbouzid12a.bib">bib</a>]      [<a href="multiboost.org">code</a>] </dl> <dl> <dt>The Stationary Subspace Analysis Toolbox</dt> <dd>Jan Saputra M眉ller, Paul von B眉nau, Frank C. Meinecke, Franz J. Kir谩ly, Klaus-Robert M眉ller; (93):3065−3069, 2011. [<a href='/papers/v12/mueller11a.html'>abs</a>][<a target=_blank href='/papers/volume12/mueller11a/mueller11a.pdf'>pdf</a>][<a href="/papers/v12/mueller11a.bib">bib</a>]      [<a href="https://github.com/paulbuenau/SSA-Toolbox">code</a>] </dl> <dl> <dt>Scikit-learn: Machine Learning in Python</dt> <dd>Fabian Pedregosa, Ga毛l Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, 脡douard Duchesnay; (85):2825−2830, 2011. [<a href='/papers/v12/pedregosa11a.html'>abs</a>][<a target=_blank href='/papers/volume12/pedregosa11a/pedregosa11a.pdf'>pdf</a>][<a href="/papers/v12/pedregosa11a.bib">bib</a>]      [<a href="https://github.com/scikit-learn/scikit-learn">code</a>] </dl> <dl> <dt>LPmade: Link Prediction Made Easy</dt> <dd>Ryan N. Lichtenwalter, Nitesh V. Chawla; (75):2489−2492, 2011. [<a href='/papers/v12/lichtenwalter11a.html'>abs</a>][<a target=_blank href='/papers/volume12/lichtenwalter11a/lichtenwalter11a.pdf'>pdf</a>][<a href="/papers/v12/lichtenwalter11a.bib">bib</a>]      [<a href="https://github.com/rlichtenwalter/LPmade">code</a>] </dl> <dl> <dt>MULAN: A Java Library for Multi-Label Learning</dt> <dd>Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Jozef Vilcek, Ioannis Vlahavas; (71):2411−2414, 2011. [<a href='/papers/v12/tsoumakas11a.html'>abs</a>][<a target=_blank href='/papers/volume12/tsoumakas11a/tsoumakas11a.pdf'>pdf</a>][<a href="/papers/v12/tsoumakas11a.bib">bib</a>]      [<a href="https://github.com/tsoumakas/mulan">code</a>] </dl> <dl> <dt>Waffles: A Machine Learning Toolkit</dt> <dd>Michael Gashler; (69):2383−2387, 2011. [<a href='/papers/v12/gashler11a.html'>abs</a>][<a target=_blank href='/papers/volume12/gashler11a/gashler11a.pdf'>pdf</a>][<a href="/papers/v12/gashler11a.bib">bib</a>]      [<a href="https://github.com/mikegashler/waffles">code</a>] </dl> <dl> <dt>MSVMpack: A Multi-Class Support Vector Machine Package</dt> <dd>Fabien Lauer, Yann Guermeur; (66):2293−2296, 2011. [<a href='/papers/v12/lauer11a.html'>abs</a>][<a target=_blank href='/papers/volume12/lauer11a/lauer11a.pdf'>pdf</a>][<a href="/papers/v12/lauer11a.bib">bib</a>]      [<a href="https://members.loria.fr/FLauer/files/MSVMpack/MSVMpack.html">code</a>] </dl> <dl> <dt>The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets</dt> <dd>Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, Christian Buchta; (57):2021−2025, 2011. [<a href='/papers/v12/hahsler11a.html'>abs</a>][<a target=_blank href='/papers/volume12/hahsler11a/hahsler11a.pdf'>pdf</a>][<a href="/papers/v12/hahsler11a.bib">bib</a>]      [<a href="https://cran.r-project.org/web/packages/arules/">code</a>] </dl> <dl> <dt>CARP: Software for Fishing Out Good Clustering Algorithms</dt> <dd>Volodymyr Melnykov, Ranjan Maitra; (3):69−73, 2011. [<a href='/papers/v12/melnykov11a.html'>abs</a>][<a target=_blank href='/papers/volume12/melnykov11a/melnykov11a.pdf'>pdf</a>][<a href="/papers/v12/melnykov11a.bib">bib</a>]      [<a href="https://maitra.public.iastate.edu/Software/CARP.html">code</a>] </dl> <dl> <dt>Gaussian Processes for Machine Learning (GPML) Toolbox</dt> <dd>Carl Edward Rasmussen, Hannes Nickisch; (100):3011−3015, 2010. [<a href='/papers/v11/rasmussen10a.html'>abs</a>][<a target=_blank href='/papers/volume11/rasmussen10a/rasmussen10a.pdf'>pdf</a>][<a href="/papers/v11/rasmussen10a.bib">bib</a>]      [<a href="http://gaussianprocess.org/gpml/code/matlab/doc/">code</a>] </dl> <dl> <dt>libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models</dt> <dd>Joris M. Mooij; (74):2169−2173, 2010. [<a href='/papers/v11/mooij10a.html'>abs</a>][<a target=_blank href='/papers/volume11/mooij10a/mooij10a.pdf'>pdf</a>][<a href="/papers/v11/mooij10a.bib">bib</a>]      [<a href="https://github.com/dbtsai/libDAI">code</a>] </dl> <dl> <dt>Model-based Boosting 2.0</dt> <dd>Torsten Hothorn, Peter B眉hlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner; (71):2109−2113, 2010. [<a href='/papers/v11/hothorn10a.html'>abs</a>][<a target=_blank href='/papers/volume11/hothorn10a/hothorn10a.pdf'>pdf</a>][<a href="/papers/v11/hothorn10a.bib">bib</a>]      [<a href="https://cran.r-project.org/web/packages/mboost/">code</a>] </dl> <dl> <dt>A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design</dt> <dd>Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom Dhaene, Karel Crombecq; (68):2051−2055, 2010. [<a href='/papers/v11/gorissen10a.html'>abs</a>][<a target=_blank href='/papers/volume11/gorissen10a/gorissen10a.pdf'>pdf</a>][<a href="/papers/v11/gorissen10a.bib">bib</a>]      [<a href="http://www.sumo.intec.ugent.be/">code</a>] </dl> <dl> <dt>The SHOGUN Machine Learning Toolbox</dt> <dd>S枚ren Sonnenburg, Gunnar R&#228;tsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio de Bona, Alexander Binder, Christian Gehl, Vojt{{\ve}}ch Franc; (60):1799−1802, 2010. [<a href='/papers/v11/sonnenburg10a.html'>abs</a>][<a target=_blank href='/papers/volume11/sonnenburg10a/sonnenburg10a.pdf'>pdf</a>][<a href="/papers/v11/sonnenburg10a.bib">bib</a>]      [<a href="https://github.com/shogun-toolbox/shogun">code</a>] </dl> <dl> <dt>FastInf: An Efficient Approximate Inference Library</dt> <dd>Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan; (57):1733−1736, 2010. [<a href='/papers/v11/jaimovich10a.html'>abs</a>][<a target=_blank href='/papers/volume11/jaimovich10a/jaimovich10a.pdf'>pdf</a>][<a href="/papers/v11/jaimovich10a.bib">bib</a>]      [<a href="http://compbio.cs.huji.ac.il/FastInf/">code</a>] </dl> <dl> <dt>MOA: Massive Online Analysis</dt> <dd>Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer; (52):1601−1604, 2010. [<a href='/papers/v11/bifet10a.html'>abs</a>][<a target=_blank href='/papers/volume11/bifet10a/bifet10a.pdf'>pdf</a>][<a href="/papers/v11/bifet10a.bib">bib</a>]      [<a href="http://moa.cs.waikato.ac.nz/">code</a>] </dl> <dl> <dt>SFO: A Toolbox for Submodular Function Optimization</dt> <dd>Andreas Krause; (38):1141−1144, 2010. [<a href='/papers/v11/krause10a.html'>abs</a>][<a target=_blank href='/papers/volume11/krause10a/krause10a.pdf'>pdf</a>][<a href="/papers/v11/krause10a.bib">bib</a>]      [<a href="https://las.inf.ethz.ch/sfo/index.html">code</a>] </dl> <dl> <dt>Continuous Time Bayesian Network Reasoning and Learning Engine</dt> <dd>Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu; (37):1137−1140, 2010. [<a href='/papers/v11/shelton10a.html'>abs</a>][<a target=_blank href='/papers/volume11/shelton10a/shelton10a.pdf'>pdf</a>][<a href="/papers/v11/shelton10a.bib">bib</a>]      [<a href="https://github.com/eudoxia0/ctbnrle">code</a>] </dl> <dl> <dt>Error-Correcting Output Codes Library</dt> <dd>Sergio Escalera, Oriol Pujol, Petia Radeva; (20):661−664, 2010. [<a href='/papers/v11/escalera10a.html'>abs</a>][<a target=_blank href='/papers/volume11/escalera10a/escalera10a.pdf'>pdf</a>][<a href="/papers/v11/escalera10a.bib">bib</a>]      [<a href="Code.zip">code</a>] </dl> <dl> <dt>DL-Learner: Learning Concepts in Description Logics</dt> <dd>Jens Lehmann; (91):2639−2642, 2009. [<a href='/papers/v10/lehmann09a.html'>abs</a>][<a target=_blank href='/papers/volume10/lehmann09a/lehmann09a.pdf'>pdf</a>][<a href="/papers/v10/lehmann09a.bib">bib</a>]      [<a href="https://dl-learner.org">code</a>] </dl> <dl> <dt>RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments</dt> <dd>Brian Tanner, Adam White; (74):2133−2136, 2009. [<a href='/papers/v10/tanner09a.html'>abs</a>][<a target=_blank href='/papers/volume10/tanner09a/tanner09a.pdf'>pdf</a>][<a href="/papers/v10/tanner09a.bib">bib</a>]      [<a href="https://glue.rl-community.org">code</a>] </dl> <dl> <dt>Dlib-ml: A Machine Learning Toolkit</dt> <dd>Davis E. King; (60):1755−1758, 2009. [<a href='/papers/v10/king09a.html'>abs</a>][<a target=_blank href='/papers/volume10/king09a/king09a.pdf'>pdf</a>][<a href="/papers/v10/king09a.bib">bib</a>]      [<a href="https://sourceforge.net/projects/dclib/">code</a>] </dl> <dl> <dt>Model Monitor (M2): Evaluating, Comparing, and Monitoring Models</dt> <dd>Troy Raeder, Nitesh V. Chawla; (47):1387−1390, 2009. [<a href='/papers/v10/raeder09a.html'>abs</a>][<a target=_blank href='/papers/volume10/raeder09a/raeder09a.pdf'>pdf</a>][<a href="/papers/v10/raeder09a.bib">bib</a>]      [<a href="https://mloss.org/software/view/137/">code</a>] </dl> <dl> <dt>Java-ML: A Machine Learning Library</dt> <dd>Thomas Abeel, Yves Van de Peer, Yvan Saeys; (34):931−934, 2009. [<a href='/papers/v10/abeel09a.html'>abs</a>][<a target=_blank href='/papers/volume10/abeel09a/abeel09a.pdf'>pdf</a>][<a href="/papers/v10/abeel09a.bib">bib</a>]      [<a href="http://java-ml.sourceforge.net">code</a>] </dl> <dl> <dt>Nieme: Large-Scale Energy-Based Models</dt> <dd>Francis Maes; (26):743−746, 2009. [<a href='/papers/v10/maes09a.html'>abs</a>][<a target=_blank href='/papers/volume10/maes09a/maes09a.pdf'>pdf</a>][<a href="/papers/v10/maes09a.bib">bib</a>]      [<a href="nieme-source-2009-03-06.tgz">code</a>] </dl> <dl> <dt>Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data</dt> <dd>Abhik Shah, Peter Woolf; (6):159−162, 2009. [<a href='/papers/v10/shah09a.html'>abs</a>][<a target=_blank href='/papers/volume10/shah09a/shah09a.pdf'>pdf</a>][<a href="/papers/v10/shah09a.bib">bib</a>]      [<a href="https://github.com/abhik/pebl">code</a>] </dl> <dl> <dt>JNCC2: The Java Implementation Of Naive Credal Classifier 2</dt> <dd>Giorgio Corani, Marco Zaffalon; (90):2695−2698, 2008. [<a href='/papers/v9/corani08b.html'>abs</a>][<a target=_blank href='/papers/volume9/corani08b/corani08b.pdf'>pdf</a>][<a href="/papers/v9/corani08b.bib">bib</a>]      [<a href="https://people.idsia.ch/~giorgio/jncc2.html">code</a>] </dl> <dl> <dt>LIBLINEAR: A Library for Large Linear Classification</dt> <dd>Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin; (61):1871−1874, 2008. [<a href='/papers/v9/fan08a.html'>abs</a>][<a target=_blank href='/papers/volume9/fan08a/fan08a.pdf'>pdf</a>][<a href="/papers/v9/fan08a.bib">bib</a>]      [<a href="https://www.csie.ntu.edu.tw/~cjlin/liblinear/">code</a>] </dl> <dl> <dt>Shark</dt> <dd>Christian Igel, Verena Heidrich-Meisner, Tobias Glasmachers; (33):993−996, 2008. [<a href='/papers/v9/igel08a.html'>abs</a>][<a target=_blank href='/papers/volume9/igel08a/igel08a.pdf'>pdf</a>][<a href="/papers/v9/igel08a.bib">bib</a>]      [<a href="https://github.com/Shark-ML/Shark/">code</a>] </dl> <dl> <dt>A Library for Locally Weighted Projection Regression</dt> <dd>Stefan Klanke, Sethu Vijayakumar, Stefan Schaal; (21):623−626, 2008. [<a href='/papers/v9/klanke08a.html'>abs</a>][<a target=_blank href='/papers/volume9/klanke08a/klanke08a.pdf'>pdf</a>][<a href="/papers/v9/klanke08a.bib">bib</a>]      [<a href="http://www.ipab.inf.ed.ac.uk/slmc/software/lwpr">code</a>] </dl> <table width="100%"> <tr> <td align="right">&copy <a target="_top" href="https://www.jmlr.org">JMLR</a> . </td> </tr> </table> </div>  <a style="font-size: 0" rel="me" href="https://sigmoid.social/@jmlr">Mastodon</a> </body> </html>