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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.03391">arXiv:2412.03391</a> <span> [<a href="https://arxiv.org/pdf/2412.03391">pdf</a>, <a href="https://arxiv.org/format/2412.03391">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Risk-aware Classification via Uncertainty Quantification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sensoy%2C+M">Murat Sensoy</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Julier%2C+S">Simon Julier</a>, <a href="/search/cs?searchtype=author&query=Saleki%2C+M">Maryam Saleki</a>, <a href="/search/cs?searchtype=author&query=Cerutti%2C+F">Federico Cerutti</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.03391v1-abstract-short" style="display: inline;"> Autonomous and semi-autonomous systems are using deep learning models to improve decision-making. However, deep classifiers can be overly confident in their incorrect predictions, a major issue especially in safety-critical domains. The present study introduces three foundational desiderata for developing real-world risk-aware classification systems. Expanding upon the previously proposed Evidenti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.03391v1-abstract-full').style.display = 'inline'; document.getElementById('2412.03391v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.03391v1-abstract-full" style="display: none;"> Autonomous and semi-autonomous systems are using deep learning models to improve decision-making. However, deep classifiers can be overly confident in their incorrect predictions, a major issue especially in safety-critical domains. The present study introduces three foundational desiderata for developing real-world risk-aware classification systems. Expanding upon the previously proposed Evidential Deep Learning (EDL), we demonstrate the unity between these principles and EDL's operational attributes. We then augment EDL empowering autonomous agents to exercise discretion during structured decision-making when uncertainty and risks are inherent. We rigorously examine empirical scenarios to substantiate these theoretical innovations. In contrast to existing risk-aware classifiers, our proposed methodologies consistently exhibit superior performance, underscoring their transformative potential in risk-conscious classification strategies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.03391v1-abstract-full').style.display = 'none'; document.getElementById('2412.03391v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication in Expert Systems with Applications</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.04734">arXiv:2407.04734</a> <span> [<a href="https://arxiv.org/pdf/2407.04734">pdf</a>, <a href="https://arxiv.org/format/2407.04734">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cominelli%2C+M">Marco Cominelli</a>, <a href="/search/cs?searchtype=author&query=Gringoli%2C+F">Francesco Gringoli</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Srivastava%2C+M+B">Mani B. Srivastava</a>, <a href="/search/cs?searchtype=author&query=Bihl%2C+T">Trevor Bihl</a>, <a href="/search/cs?searchtype=author&query=Blasch%2C+E+P">Erik P. Blasch</a>, <a href="/search/cs?searchtype=author&query=Iyer%2C+N">Nandini Iyer</a>, <a href="/search/cs?searchtype=author&query=Cerutti%2C+F">Federico Cerutti</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.04734v1-abstract-short" style="display: inline;"> Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body's interaction with electromagnetic signals. Current Wi-Fi sensing applications predominantly employ data-driven learning techniques to associate the fluctuations in the physical properties of the communication channel with the human activity causing them. However, these techniques often… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.04734v1-abstract-full').style.display = 'inline'; document.getElementById('2407.04734v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.04734v1-abstract-full" style="display: none;"> Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body's interaction with electromagnetic signals. Current Wi-Fi sensing applications predominantly employ data-driven learning techniques to associate the fluctuations in the physical properties of the communication channel with the human activity causing them. However, these techniques often lack the desired flexibility and transparency. This paper introduces DeepProbHAR, a neuro-symbolic architecture for Wi-Fi sensing, providing initial evidence that Wi-Fi signals can differentiate between simple movements, such as leg or arm movements, which are integral to human activities like running or walking. The neuro-symbolic approach affords gathering such evidence without needing additional specialised data collection or labelling. The training of DeepProbHAR is facilitated by declarative domain knowledge obtained from a camera feed and by fusing signals from various antennas of the Wi-Fi receivers. DeepProbHAR achieves results comparable to the state-of-the-art in human activity recognition. Moreover, as a by-product of the learning process, DeepProbHAR generates specialised classifiers for simple movements that match the accuracy of models trained on finely labelled datasets, which would be particularly costly. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.04734v1-abstract-full').style.display = 'none'; document.getElementById('2407.04734v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 9 figures, accepted at 27th International Conference on Information Fusion (FUSION 2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.04733">arXiv:2407.04733</a> <span> [<a href="https://arxiv.org/pdf/2407.04733">pdf</a>, <a href="https://arxiv.org/format/2407.04733">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.23919/FUSION52260.2023.10224098">10.23919/FUSION52260.2023.10224098 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cominelli%2C+M">Marco Cominelli</a>, <a href="/search/cs?searchtype=author&query=Gringoli%2C+F">Francesco Gringoli</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Srivastava%2C+M+B">Mani B. Srivastava</a>, <a href="/search/cs?searchtype=author&query=Cerutti%2C+F">Federico Cerutti</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.04733v1-abstract-short" style="display: inline;"> Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a principled architecture which employs Variational Auto-Encoders for estimating a latent distribution responsible for generating the data, and Evidential Deep Learning for its ability to sense out-of-distribution activities… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.04733v1-abstract-full').style.display = 'inline'; document.getElementById('2407.04733v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.04733v1-abstract-full" style="display: none;"> Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a principled architecture which employs Variational Auto-Encoders for estimating a latent distribution responsible for generating the data, and Evidential Deep Learning for its ability to sense out-of-distribution activities. We verify that the fused data processed by different antennas of the same Wi-Fi receiver results in increased accuracy of human activity recognition compared with the most recent benchmarks, while still being informative when facing out-of-distribution samples and enabling semantic interpretation of latent variables in terms of physical phenomena. The results of this paper are a first contribution toward the ultimate goal of providing a flexible, semantic characterisation of black-swan events, i.e., events for which we have limited to no training data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.04733v1-abstract-full').style.display = 'none'; document.getElementById('2407.04733v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 10 figures, accepted at 26th International Conference on Information Fusion (FUSION 2023)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.18826">arXiv:2404.18826</a> <span> [<a href="https://arxiv.org/pdf/2404.18826">pdf</a>, <a href="https://arxiv.org/format/2404.18826">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Winning the Social Media Influence Battle: Uncertainty-Aware Opinions to Understand and Spread True Information via Competitive Influence Maximization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+Q">Qi Zhang</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=J%C3%B8sang%2C+A">Audun J酶sang</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+D+H">Dong Hyun. Jeong</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+F">Feng Chen</a>, <a href="/search/cs?searchtype=author&query=Cho%2C+J">Jin-Hee Cho</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.18826v2-abstract-short" style="display: inline;"> Competitive Influence Maximization (CIM) involves entities competing to maximize influence in online social networks (OSNs). Current Deep Reinforcement Learning (DRL) methods in CIM rely on simplistic binary opinion models (i.e., an opinion is represented by either 0 or 1) and often overlook the complexity of users' behavioral characteristics and their prior knowledge. We propose a novel DRL-based… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.18826v2-abstract-full').style.display = 'inline'; document.getElementById('2404.18826v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.18826v2-abstract-full" style="display: none;"> Competitive Influence Maximization (CIM) involves entities competing to maximize influence in online social networks (OSNs). Current Deep Reinforcement Learning (DRL) methods in CIM rely on simplistic binary opinion models (i.e., an opinion is represented by either 0 or 1) and often overlook the complexity of users' behavioral characteristics and their prior knowledge. We propose a novel DRL-based framework that enhances CIM analysis by integrating Subjective Logic (SL) to accommodate uncertain opinions, users' behaviors, and their preferences. This approach targets the mitigation of false information by effectively propagating true information. By modeling two competitive agents, one spreading true information and the other spreading false information, we capture the strategic interplay essential to CIM. Our framework utilizes an uncertainty-based opinion model (UOM) to assess the impact on information quality in OSNs, emphasizing the importance of user behavior alongside network topology in selecting influential seed nodes. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods, achieving faster and more influential results (i.e., outperforming over 20%) under realistic network conditions. Moreover, our method shows robust performance in partially observable networks, effectively doubling the performance when users are predisposed to disbelieve true information. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.18826v2-abstract-full').style.display = 'none'; document.getElementById('2404.18826v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 3 figures, submitted to ASONAM 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.10980">arXiv:2404.10980</a> <span> [<a href="https://arxiv.org/pdf/2404.10980">pdf</a>, <a href="https://arxiv.org/format/2404.10980">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+C">Changbin Li</a>, <a href="/search/cs?searchtype=author&query=Li%2C+K">Kangshuo Li</a>, <a href="/search/cs?searchtype=author&query=Ou%2C+Y">Yuzhe Ou</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=J%C3%B8sang%2C+A">Audun J酶sang</a>, <a href="/search/cs?searchtype=author&query=Cho%2C+J">Jin-Hee Cho</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+D+H">Dong Hyun Jeong</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+F">Feng Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.10980v1-abstract-short" style="display: inline;"> Deep neural networks (DNNs) have been shown to perform well on exclusive, multi-class classification tasks. However, when different classes have similar visual features, it becomes challenging for human annotators to differentiate them. This scenario necessitates the use of composite class labels. In this paper, we propose a novel framework called Hyper-Evidential Neural Network (HENN) that explic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.10980v1-abstract-full').style.display = 'inline'; document.getElementById('2404.10980v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.10980v1-abstract-full" style="display: none;"> Deep neural networks (DNNs) have been shown to perform well on exclusive, multi-class classification tasks. However, when different classes have similar visual features, it becomes challenging for human annotators to differentiate them. This scenario necessitates the use of composite class labels. In this paper, we propose a novel framework called Hyper-Evidential Neural Network (HENN) that explicitly models predictive uncertainty due to composite class labels in training data in the context of the belief theory called Subjective Logic (SL). By placing a grouped Dirichlet distribution on the class probabilities, we treat predictions of a neural network as parameters of hyper-subjective opinions and learn the network that collects both single and composite evidence leading to these hyper-opinions by a deterministic DNN from data. We introduce a new uncertainty type called vagueness originally designed for hyper-opinions in SL to quantify composite classification uncertainty for DNNs. Our results demonstrate that HENN outperforms its state-of-the-art counterparts based on four image datasets. The code and datasets are available at: https://github.com/Hugo101/HyperEvidentialNN. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.10980v1-abstract-full').style.display = 'none'; document.getElementById('2404.10980v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">In Proceedings of The Twelfth International Conference on Learning Representations, ICLR 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.14136">arXiv:2402.14136</a> <span> [<a href="https://arxiv.org/pdf/2402.14136">pdf</a>, <a href="https://arxiv.org/format/2402.14136">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jeong%2C+H+L">Ho Lyun Jeong</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Ziqi Wang</a>, <a href="/search/cs?searchtype=author&query=Samplawski%2C+C">Colin Samplawski</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+J">Jason Wu</a>, <a href="/search/cs?searchtype=author&query=Fang%2C+S">Shiwei Fang</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Ganesan%2C+D">Deepak Ganesan</a>, <a href="/search/cs?searchtype=author&query=Marlin%2C+B">Benjamin Marlin</a>, <a href="/search/cs?searchtype=author&query=Srivastava%2C+M">Mani Srivastava</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.14136v1-abstract-short" style="display: inline;"> Constantly locating moving objects, i.e., geospatial tracking, is essential for autonomous building infrastructure. Accurate and robust geospatial tracking often leverages multimodal sensor fusion algorithms, which require large datasets with time-aligned, synchronized data from various sensor types. However, such datasets are not readily available. Hence, we propose GDTM, a nine-hour dataset for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.14136v1-abstract-full').style.display = 'inline'; document.getElementById('2402.14136v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.14136v1-abstract-full" style="display: none;"> Constantly locating moving objects, i.e., geospatial tracking, is essential for autonomous building infrastructure. Accurate and robust geospatial tracking often leverages multimodal sensor fusion algorithms, which require large datasets with time-aligned, synchronized data from various sensor types. However, such datasets are not readily available. Hence, we propose GDTM, a nine-hour dataset for multimodal object tracking with distributed multimodal sensors and reconfigurable sensor node placements. Our dataset enables the exploration of several research problems, such as optimizing architectures for processing multimodal data, and investigating models' robustness to adverse sensing conditions and sensor placement variances. A GitHub repository containing the code, sample data, and checkpoints of this work is available at https://github.com/nesl/GDTM. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.14136v1-abstract-full').style.display = 'none'; document.getElementById('2402.14136v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.12663">arXiv:2310.12663</a> <span> [<a href="https://arxiv.org/pdf/2310.12663">pdf</a>, <a href="https://arxiv.org/format/2310.12663">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Knowledge from Uncertainty in Evidential Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Davies%2C+C">Cai Davies</a>, <a href="/search/cs?searchtype=author&query=Vilamala%2C+M+R">Marc Roig Vilamala</a>, <a href="/search/cs?searchtype=author&query=Preece%2C+A+D">Alun D. Preece</a>, <a href="/search/cs?searchtype=author&query=Cerutti%2C+F">Federico Cerutti</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Chakraborty%2C+S">Supriyo Chakraborty</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.12663v1-abstract-short" style="display: inline;"> This work reveals an evidential signal that emerges from the uncertainty value in Evidential Deep Learning (EDL). EDL is one example of a class of uncertainty-aware deep learning approaches designed to provide confidence (or epistemic uncertainty) about the current test sample. In particular for computer vision and bidirectional encoder large language models, the `evidential signal' arising from t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12663v1-abstract-full').style.display = 'inline'; document.getElementById('2310.12663v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.12663v1-abstract-full" style="display: none;"> This work reveals an evidential signal that emerges from the uncertainty value in Evidential Deep Learning (EDL). EDL is one example of a class of uncertainty-aware deep learning approaches designed to provide confidence (or epistemic uncertainty) about the current test sample. In particular for computer vision and bidirectional encoder large language models, the `evidential signal' arising from the Dirichlet strength in EDL can, in some cases, discriminate between classes, which is particularly strong when using large language models. We hypothesise that the KL regularisation term causes EDL to couple aleatoric and epistemic uncertainty. In this paper, we empirically investigate the correlations between misclassification and evaluated uncertainty, and show that EDL's `evidential signal' is due to misclassification bias. We critically evaluate EDL with other Dirichlet-based approaches, namely Generative Evidential Neural Networks (EDL-GEN) and Prior Networks, and show theoretically and empirically the differences between these loss functions. We conclude that EDL's coupling of uncertainty arises from these differences due to the use (or lack) of out-of-distribution samples during training. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12663v1-abstract-full').style.display = 'none'; document.getElementById('2310.12663v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.10195">arXiv:2302.10195</a> <span> [<a href="https://arxiv.org/pdf/2302.10195">pdf</a>, <a href="https://arxiv.org/format/2302.10195">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Guo%2C+Z">Zhen Guo</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Q">Qi Zhang</a>, <a href="/search/cs?searchtype=author&query=An%2C+X">Xinwei An</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Q">Qisheng Zhang</a>, <a href="/search/cs?searchtype=author&query=J%C3%B8sang%2C+A">Audun J酶sang</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+F">Feng Chen</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+D+H">Dong H. Jeong</a>, <a href="/search/cs?searchtype=author&query=Cho%2C+J">Jin-Hee Cho</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.10195v1-abstract-short" style="display: inline;"> Due to various and serious adverse impacts of spreading fake news, it is often known that only people with malicious intent would propagate fake news. However, it is not necessarily true based on social science studies. Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with differ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.10195v1-abstract-full').style.display = 'inline'; document.getElementById('2302.10195v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.10195v1-abstract-full" style="display: none;"> Due to various and serious adverse impacts of spreading fake news, it is often known that only people with malicious intent would propagate fake news. However, it is not necessarily true based on social science studies. Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with different approaches. To this end, we propose an intent classification framework that can best identify the correct intent of fake news. We will leverage deep reinforcement learning (DRL) that can optimize the structural representation of each tweet by removing noisy words from the input sequence when appending an actor to the long short-term memory (LSTM) intent classifier. Policy gradient DRL model (e.g., REINFORCE) can lead the actor to a higher delayed reward. We also devise a new uncertainty-aware immediate reward using a subjective opinion that can explicitly deal with multidimensional uncertainty for effective decision-making. Via 600K training episodes from a fake news tweets dataset with an annotated intent class, we evaluate the performance of uncertainty-aware reward in DRL. Evaluation results demonstrate that our proposed framework efficiently reduces the number of selected words to maintain a high 95\% multi-class accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.10195v1-abstract-full').style.display = 'none'; document.getElementById('2302.10195v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted version</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.06343">arXiv:2212.06343</a> <span> [<a href="https://arxiv.org/pdf/2212.06343">pdf</a>, <a href="https://arxiv.org/format/2212.06343">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+Q">Qisheng Zhang</a>, <a href="/search/cs?searchtype=author&query=Guo%2C+Z">Zhen Guo</a>, <a href="/search/cs?searchtype=author&query=J%C3%B8sang%2C+A">Audun J酶sang</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+F">Feng Chen</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+D+H">Dong H. Jeong</a>, <a href="/search/cs?searchtype=author&query=Cho%2C+J">Jin-Hee Cho</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.06343v1-abstract-short" style="display: inline;"> Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL) approach. However, we observe that the homogeneous exploration process in PPO could cause an unexpected stability issue in the training phase. To address this issue, we propose PPO-UE, a PPO variant equipped with self-adaptive uncertainty-aware explorations (UEs) based on a ratio uncertainty level… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.06343v1-abstract-full').style.display = 'inline'; document.getElementById('2212.06343v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.06343v1-abstract-full" style="display: none;"> Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL) approach. However, we observe that the homogeneous exploration process in PPO could cause an unexpected stability issue in the training phase. To address this issue, we propose PPO-UE, a PPO variant equipped with self-adaptive uncertainty-aware explorations (UEs) based on a ratio uncertainty level. The proposed PPO-UE is designed to improve convergence speed and performance with an optimized ratio uncertainty level. Through extensive sensitivity analysis by varying the ratio uncertainty level, our proposed PPO-UE considerably outperforms the baseline PPO in Roboschool continuous control tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.06343v1-abstract-full').style.display = 'none'; document.getElementById('2212.06343v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.10932">arXiv:2208.10932</a> <span> [<a href="https://arxiv.org/pdf/2208.10932">pdf</a>, <a href="https://arxiv.org/format/2208.10932">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Research Note on Uncertain Probabilities and Abstract Argumentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Baroni%2C+P">Pietro Baroni</a>, <a href="/search/cs?searchtype=author&query=Cerutti%2C+F">Federico Cerutti</a>, <a href="/search/cs?searchtype=author&query=Giacomin%2C+M">Massimiliano Giacomin</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Sensoy%2C+M">Murat Sensoy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.10932v1-abstract-short" style="display: inline;"> The sixth assessment of the international panel on climate change (IPCC) states that "cumulative net CO2 emissions over the last decade (2010-2019) are about the same size as the 11 remaining carbon budget likely to limit warming to 1.5C (medium confidence)." Such reports directly feed the public discourse, but nuances such as the degree of belief and of confidence are often lost. In this paper, w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.10932v1-abstract-full').style.display = 'inline'; document.getElementById('2208.10932v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.10932v1-abstract-full" style="display: none;"> The sixth assessment of the international panel on climate change (IPCC) states that "cumulative net CO2 emissions over the last decade (2010-2019) are about the same size as the 11 remaining carbon budget likely to limit warming to 1.5C (medium confidence)." Such reports directly feed the public discourse, but nuances such as the degree of belief and of confidence are often lost. In this paper, we propose a formal account for allowing such degrees of belief and the associated confidence to be used to label arguments in abstract argumentation settings. Differently from other proposals in probabilistic argumentation, we focus on the task of probabilistic inference over a chosen query building upon Sato's distribution semantics which has been already shown to encompass a variety of cases including the semantics of Bayesian networks. Borrowing from the vast literature on such semantics, we examine how such tasks can be dealt with in practice when considering uncertain probabilities, and discuss the connections with existing proposals for probabilistic argumentation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.10932v1-abstract-full').style.display = 'none'; document.getElementById('2208.10932v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.07368">arXiv:2208.07368</a> <span> [<a href="https://arxiv.org/pdf/2208.07368">pdf</a>, <a href="https://arxiv.org/format/2208.07368">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> SOLBP: Second-Order Loopy Belief Propagation for Inference in Uncertain Bayesian Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hougen%2C+C+D">Conrad D. Hougen</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Ivanovska%2C+M">Magdalena Ivanovska</a>, <a href="/search/cs?searchtype=author&query=Cerutti%2C+F">Federico Cerutti</a>, <a href="/search/cs?searchtype=author&query=Mishra%2C+K+V">Kumar Vijay Mishra</a>, <a href="/search/cs?searchtype=author&query=Hero%2C+A+O">Alfred O. Hero III</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.07368v1-abstract-short" style="display: inline;"> In second-order uncertain Bayesian networks, the conditional probabilities are only known within distributions, i.e., probabilities over probabilities. The delta-method has been applied to extend exact first-order inference methods to propagate both means and variances through sum-product networks derived from Bayesian networks, thereby characterizing epistemic uncertainty, or the uncertainty in t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.07368v1-abstract-full').style.display = 'inline'; document.getElementById('2208.07368v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.07368v1-abstract-full" style="display: none;"> In second-order uncertain Bayesian networks, the conditional probabilities are only known within distributions, i.e., probabilities over probabilities. The delta-method has been applied to extend exact first-order inference methods to propagate both means and variances through sum-product networks derived from Bayesian networks, thereby characterizing epistemic uncertainty, or the uncertainty in the model itself. Alternatively, second-order belief propagation has been demonstrated for polytrees but not for general directed acyclic graph structures. In this work, we extend Loopy Belief Propagation to the setting of second-order Bayesian networks, giving rise to Second-Order Loopy Belief Propagation (SOLBP). For second-order Bayesian networks, SOLBP generates inferences consistent with those generated by sum-product networks, while being more computationally efficient and scalable. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.07368v1-abstract-full').style.display = 'none'; document.getElementById('2208.07368v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, appeared at FUSION 2022: 25th International Conference on Information Fusion</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.04221">arXiv:2208.04221</a> <span> [<a href="https://arxiv.org/pdf/2208.04221">pdf</a>, <a href="https://arxiv.org/format/2208.04221">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/MLSP52302.2021.9596205">10.1109/MLSP52302.2021.9596205 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Uncertain Bayesian Networks: Learning from Incomplete Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hougen%2C+C+D">Conrad D. Hougen</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Cerutti%2C+F">Federico Cerutti</a>, <a href="/search/cs?searchtype=author&query=Hero%2C+A+O">Alfred O. Hero III</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.04221v1-abstract-short" style="display: inline;"> When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order estimation methods provide a framework for both estimating the probabilities and quantifying the uncertainty in these estimates. We refer to these cases as uncer tain or second-order Bayesian networks. When such data are com… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.04221v1-abstract-full').style.display = 'inline'; document.getElementById('2208.04221v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.04221v1-abstract-full" style="display: none;"> When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order estimation methods provide a framework for both estimating the probabilities and quantifying the uncertainty in these estimates. We refer to these cases as uncer tain or second-order Bayesian networks. When such data are complete, i.e., all variable values are observed for each instantiation, the conditional probabilities are known to be Dirichlet-distributed. This paper improves the current state-of-the-art approaches for handling uncertain Bayesian networks by enabling them to learn distributions for their parameters, i.e., conditional probabilities, with incomplete data. We extensively evaluate various methods to learn the posterior of the parameters through the desired and empirically derived strength of confidence bounds for various queries. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.04221v1-abstract-full').style.display = 'none'; document.getElementById('2208.04221v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, appeared at 2021 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021, pp. 1-6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.05675">arXiv:2206.05675</a> <span> [<a href="https://arxiv.org/pdf/2206.05675">pdf</a>, <a href="https://arxiv.org/format/2206.05675">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Guo%2C+Z">Zhen Guo</a>, <a href="/search/cs?searchtype=author&query=Wan%2C+Z">Zelin Wan</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Q">Qisheng Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+X">Xujiang Zhao</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+F">Feng Chen</a>, <a href="/search/cs?searchtype=author&query=Cho%2C+J">Jin-Hee Cho</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Q">Qi Zhang</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+D+H">Dong H. Jeong</a>, <a href="/search/cs?searchtype=author&query=J%C3%B8sang%2C+A">Audun J酶sang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2206.05675v2-abstract-short" style="display: inline;"> An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Deep/machine learning (ML/DL) has been hugely leveraged to solve complex problems involved with processing high-dimensional data. However, reasoning and quantifying different types of uncertainties to achieve effective decision-making have been much less explored in ML/DL than in other Artif… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.05675v2-abstract-full').style.display = 'inline'; document.getElementById('2206.05675v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.05675v2-abstract-full" style="display: none;"> An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Deep/machine learning (ML/DL) has been hugely leveraged to solve complex problems involved with processing high-dimensional data. However, reasoning and quantifying different types of uncertainties to achieve effective decision-making have been much less explored in ML/DL than in other Artificial Intelligence (AI) domains. In particular, belief/evidence theories have been studied in KRR since the 1960s to reason and measure uncertainties to enhance decision-making effectiveness. We found that only a few studies have leveraged the mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty. In this survey paper, we discuss several popular belief theories and their core ideas dealing with uncertainty causes and types and quantifying them, along with the discussions of their applicability in ML/DL. In addition, we discuss three main approaches that leverage belief theories in Deep Neural Networks (DNNs), including Evidential DNNs, Fuzzy DNNs, and Rough DNNs, in terms of their uncertainty causes, types, and quantification methods along with their applicability in diverse problem domains. Based on our in-depth survey, we discuss insights, lessons learned, limitations of the current state-of-the-art bridging belief theories and ML/DL, and finally, future research directions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.05675v2-abstract-full').style.display = 'none'; document.getElementById('2206.05675v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">First four authors contributed equally</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.10865">arXiv:2102.10865</a> <span> [<a href="https://arxiv.org/pdf/2102.10865">pdf</a>, <a href="https://arxiv.org/format/2102.10865">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cerutti%2C+F">Federico Cerutti</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Kimmig%2C+A">Angelika Kimmig</a>, <a href="/search/cs?searchtype=author&query=Sensoy%2C+M">Murat Sensoy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.10865v1-abstract-short" style="display: inline;"> When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian approaches for probabilistic reasoning in order to determine the confidence (or epistemic uncertainty) in the probabilities in light of the training data. We propose an approach to ove… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.10865v1-abstract-full').style.display = 'inline'; document.getElementById('2102.10865v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.10865v1-abstract-full" style="display: none;"> When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian approaches for probabilistic reasoning in order to determine the confidence (or epistemic uncertainty) in the probabilities in light of the training data. We propose an approach to overcome the independence assumption behind most of the approaches dealing with a large class of probabilistic reasoning that includes Bayesian networks as well as several instances of probabilistic logic. We provide an algorithm for Bayesian learning from sparse, albeit complete, observations, and for deriving inferences and their confidences keeping track of the dependencies between variables when they are manipulated within the unifying computational formalism provided by probabilistic circuits. Each leaf of such circuits is labelled with a beta-distributed random variable that provides us with an elegant framework for representing uncertain probabilities. We achieve better estimation of epistemic uncertainty than state-of-the-art approaches, including highly engineered ones, while being able to handle general circuits and with just a modest increase in the computational effort compared to using point probabilities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.10865v1-abstract-full').style.display = 'none'; document.getElementById('2102.10865v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Under submission to MACH</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1908.05387">arXiv:1908.05387</a> <span> [<a href="https://arxiv.org/pdf/1908.05387">pdf</a>, <a href="https://arxiv.org/format/1908.05387">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1089/big.2019.0169">10.1089/big.2019.0169 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> HONEM: Learning Embedding for Higher Order Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Saebi%2C+M">Mandana Saebi</a>, <a href="/search/cs?searchtype=author&query=Ciampaglia%2C+G+L">Giovanni Luca Ciampaglia</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M Kaplan</a>, <a href="/search/cs?searchtype=author&query=Chawla%2C+N+V">Nitesh V Chawla</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1908.05387v2-abstract-short" style="display: inline;"> Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network (FON), that is, the network that only captures the pairwise interactions between the nodes. As a result, these… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.05387v2-abstract-full').style.display = 'inline'; document.getElementById('1908.05387v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1908.05387v2-abstract-full" style="display: none;"> Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network (FON), that is, the network that only captures the pairwise interactions between the nodes. As a result, these methods may fail to incorporate non-Markovian higher-order dependencies in the network. Thus, the embeddings that are generated may not accurately represent of the underlying phenomena in a network, resulting in inferior performance in different inductive or transductive learning tasks. To address this challenge, this paper presents HONEM, a higher-order network embedding method that captures the non-Markovian higher-order dependencies in a network. HONEM is specifically designed for the higher-order network structure (HON) and outperforms other state-of-the-art methods in node classification, network re-construction, link prediction, and visualization for networks that contain non-Markovian higher-order dependencies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.05387v2-abstract-full').style.display = 'none'; document.getElementById('1908.05387v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Big Data 8, no. 4 (2020): 255-269 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1712.09658">arXiv:1712.09658</a> <span> [<a href="https://arxiv.org/pdf/1712.09658">pdf</a>, <a href="https://arxiv.org/format/1712.09658">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1140/epjds/s13688-020-00233-y">10.1140/epjds/s13688-020-00233-y <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Saebi%2C+M">Mandana Saebi</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+J">Jian Xu</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Ribeiro%2C+B">Bruno Ribeiro</a>, <a href="/search/cs?searchtype=author&query=Chawla%2C+N+V">Nitesh V. Chawla</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1712.09658v3-abstract-short" style="display: inline;"> Complex systems, represented as dynamic networks, comprise of components that influence each other via direct and/or indirect interactions. Recent research has shown the importance of using Higher-Order Networks (HONs) for modeling and analyzing such complex systems, as the typical Markovian assumption in developing the First Order Network (FON) can be limiting. This higher-order network represent… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.09658v3-abstract-full').style.display = 'inline'; document.getElementById('1712.09658v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1712.09658v3-abstract-full" style="display: none;"> Complex systems, represented as dynamic networks, comprise of components that influence each other via direct and/or indirect interactions. Recent research has shown the importance of using Higher-Order Networks (HONs) for modeling and analyzing such complex systems, as the typical Markovian assumption in developing the First Order Network (FON) can be limiting. This higher-order network representation not only creates a more accurate representation of the underlying complex system, but also leads to more accurate network analysis. In this paper, we first present a scalable and accurate model, BuildHON+, for higher-order network representation of data derived from a complex system with various orders of dependencies. Then, we show that this higher-order network representation modeled by BuildHON+ is significantly more accurate in identifying anomalies than FON, demonstrating a need for the higher-order network representation and modeling of complex systems for deriving meaningful conclusions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.09658v3-abstract-full').style.display = 'none'; document.getElementById('1712.09658v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 December, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">EPJ Data Science, 27 pages, 11 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> EPJ Data Sci., 9 1 (2020) 15 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1705.05424">arXiv:1705.05424</a> <span> [<a href="https://arxiv.org/pdf/1705.05424">pdf</a>, <a href="https://arxiv.org/ps/1705.05424">ps</a>, <a href="https://arxiv.org/format/1705.05424">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TSP.2018.2802459">10.1109/TSP.2018.2802459 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Attack Detection in Sensor Network Target Localization Systems with Quantized Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jiangfan Zhang</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+X">Xiaodong Wang</a>, <a href="/search/cs?searchtype=author&query=Blum%2C+R+S">Rick S. Blum</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1705.05424v1-abstract-short" style="display: inline;"> We consider a sensor network focused on target localization, where sensors measure the signal strength emitted from the target. Each measurement is quantized to one bit and sent to the fusion center. A general attack is considered at some sensors that attempts to cause the fusion center to produce an inaccurate estimation of the target location with a large mean-square-error. The attack is a combi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.05424v1-abstract-full').style.display = 'inline'; document.getElementById('1705.05424v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1705.05424v1-abstract-full" style="display: none;"> We consider a sensor network focused on target localization, where sensors measure the signal strength emitted from the target. Each measurement is quantized to one bit and sent to the fusion center. A general attack is considered at some sensors that attempts to cause the fusion center to produce an inaccurate estimation of the target location with a large mean-square-error. The attack is a combination of man-in-the-middle, hacking, and spoofing attacks that can effectively change both signals going into and coming out of the sensor nodes in a realistic manner. We show that the essential effect of attacks is to alter the estimated distance between the target and each attacked sensor to a different extent, giving rise to a geometric inconsistency among the attacked and unattacked sensors. Hence, with the help of two secure sensors, a class of detectors are proposed to detect the attacked sensors by scrutinizing the existence of the geometric inconsistency. We show that the false alarm and miss probabilities of the proposed detectors decrease exponentially as the number of measurement samples increases, which implies that for sufficiently large number of samples, the proposed detectors can identify the attacked and unattacked sensors with any required accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.05424v1-abstract-full').style.display = 'none'; document.getElementById('1705.05424v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 May, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1703.04213">arXiv:1703.04213</a> <span> [<a href="https://arxiv.org/pdf/1703.04213">pdf</a>, <a href="https://arxiv.org/format/1703.04213">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> MetaPAD: Meta Pattern Discovery from Massive Text Corpora </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jiang%2C+M">Meng Jiang</a>, <a href="/search/cs?searchtype=author&query=Shang%2C+J">Jingbo Shang</a>, <a href="/search/cs?searchtype=author&query=Cassidy%2C+T">Taylor Cassidy</a>, <a href="/search/cs?searchtype=author&query=Ren%2C+X">Xiang Ren</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Hanratty%2C+T+P">Timothy P. Hanratty</a>, <a href="/search/cs?searchtype=author&query=Han%2C+J">Jiawei Han</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1703.04213v2-abstract-short" style="display: inline;"> Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency parsing-based pattern discovery approach. However, the parsing results lose rich context around entities in the patterns, and the process is costly for a corpus of large scale. In this study, we propose a novel typed tex… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.04213v2-abstract-full').style.display = 'inline'; document.getElementById('1703.04213v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1703.04213v2-abstract-full" style="display: none;"> Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency parsing-based pattern discovery approach. However, the parsing results lose rich context around entities in the patterns, and the process is costly for a corpus of large scale. In this study, we propose a novel typed textual pattern structure, called meta pattern, which is extended to a frequent, informative, and precise subsequence pattern in certain context. We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise. Experiments demonstrate that our proposed framework discovers high-quality typed textual patterns efficiently from different genres of massive corpora and facilitates information extraction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.04213v2-abstract-full').style.display = 'none'; document.getElementById('1703.04213v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1610.09769">arXiv:1610.09769</a> <span> [<a href="https://arxiv.org/pdf/1610.09769">pdf</a>, <a href="https://arxiv.org/format/1610.09769">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shang%2C+J">Jingbo Shang</a>, <a href="/search/cs?searchtype=author&query=Qu%2C+M">Meng Qu</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+J">Jialu Liu</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a>, <a href="/search/cs?searchtype=author&query=Han%2C+J">Jiawei Han</a>, <a href="/search/cs?searchtype=author&query=Peng%2C+J">Jian Peng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1610.09769v1-abstract-short" style="display: inline;"> Most real-world data can be modeled as heterogeneous information networks (HINs) consisting of vertices of multiple types and their relationships. Search for similar vertices of the same type in large HINs, such as bibliographic networks and business-review networks, is a fundamental problem with broad applications. Although similarity search in HINs has been studied previously, most existing appr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1610.09769v1-abstract-full').style.display = 'inline'; document.getElementById('1610.09769v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1610.09769v1-abstract-full" style="display: none;"> Most real-world data can be modeled as heterogeneous information networks (HINs) consisting of vertices of multiple types and their relationships. Search for similar vertices of the same type in large HINs, such as bibliographic networks and business-review networks, is a fundamental problem with broad applications. Although similarity search in HINs has been studied previously, most existing approaches neither explore rich semantic information embedded in the network structures nor take user's preference as a guidance. In this paper, we re-examine similarity search in HINs and propose a novel embedding-based framework. It models vertices as low-dimensional vectors to explore network structure-embedded similarity. To accommodate user preferences at defining similarity semantics, our proposed framework, ESim, accepts user-defined meta-paths as guidance to learn vertex vectors in a user-preferred embedding space. Moreover, an efficient and parallel sampling-based optimization algorithm has been developed to learn embeddings in large-scale HINs. Extensive experiments on real-world large-scale HINs demonstrate a significant improvement on the effectiveness of ESim over several state-of-the-art algorithms as well as its scalability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1610.09769v1-abstract-full').style.display = 'none'; document.getElementById('1610.09769v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 October, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2016. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1208.0221">arXiv:1208.0221</a> <span> [<a href="https://arxiv.org/pdf/1208.0221">pdf</a>, <a href="https://arxiv.org/format/1208.0221">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> </div> </div> <p class="title is-5 mathjax"> Measuring Two-Event Structural Correlations on Graphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Guan%2C+Z">Ziyu Guan</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+X">Xifeng Yan</a>, <a href="/search/cs?searchtype=author&query=Kaplan%2C+L+M">Lance M. Kaplan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1208.0221v1-abstract-short" style="display: inline;"> Real-life graphs usually have various kinds of events happening on them, e.g., product purchases in online social networks and intrusion alerts in computer networks. The occurrences of events on the same graph could be correlated, exhibiting either attraction or repulsion. Such structural correlations can reveal important relationships between different events. Unfortunately, correlation relations… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1208.0221v1-abstract-full').style.display = 'inline'; document.getElementById('1208.0221v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1208.0221v1-abstract-full" style="display: none;"> Real-life graphs usually have various kinds of events happening on them, e.g., product purchases in online social networks and intrusion alerts in computer networks. The occurrences of events on the same graph could be correlated, exhibiting either attraction or repulsion. Such structural correlations can reveal important relationships between different events. Unfortunately, correlation relationships on graph structures are not well studied and cannot be captured by traditional measures. In this work, we design a novel measure for assessing two-event structural correlations on graphs. Given the occurrences of two events, we choose uniformly a sample of "reference nodes" from the vicinity of all event nodes and employ the Kendall's tau rank correlation measure to compute the average concordance of event density changes. Significance can be efficiently assessed by tau's nice property of being asymptotically normal under the null hypothesis. In order to compute the measure in large scale networks, we develop a scalable framework using different sampling strategies. The complexity of these strategies is analyzed. Experiments on real graph datasets with both synthetic and real events demonstrate that the proposed framework is not only efficacious, but also efficient and scalable. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1208.0221v1-abstract-full').style.display = 'none'; document.getElementById('1208.0221v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 August, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2012. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">VLDB2012</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of the VLDB Endowment (PVLDB), Vol. 5, No. 11, pp. 1400-1411 (2012) </p> </li> </ol> 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