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Explainable Artificial Intelligence (XAI) – Tractable Probabilistic Logic Models

Papers

2020

  • [ p71 ] Roy, Chiradeep; Nourani, Mahsan; Shanbhag, Mahesh; Rahman, Tahrima; Ragan, Eric; Ruozzi, Nicholas; and Gogate, Vibhav. “Interactive Visual Analytics for Making Explainable and Accountable Decisions”, In Transactions on Interactive Intelligent Systems 2020 (Under Review). [PDF]

  • [ p70] Nourani, M., Honeycutt, D., Block, J., Roy, C., Rahman, T., Ragan, E., and Gogate, V. (2020, to appear). Investigating the Importance of First Impressions and Explainable AI with Interactive Video Analysis. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (ACM CHI 2020) pp. 1-8. [PDF]

  • [ p69] Anji Liu, Yitao Liang and Guy Van den Broeck. “Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration”, In Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). [PDF]

  • [ p68] Laura I. Galindez Olascoaga, Wannes Meert, Nimish Shah, Guy Van den Broeck and Marian Verhelst. “Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams”, In Proceedings of the Symposium on Intelligent Data Analysis (IDA). [PDF]

  • [ p67] Tal Friedman and Guy Van den Broeck. “Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings”, In Ninth International Workshop on Statistical Relational AI (StarAI). [PDF]

  • [ p66] YooJung Choi, Golnoosh Farnadi, Behrouz Babaki and Guy Van den Broeck. “Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns”, In Proceedings of the 34th AAAI Conference on Artificial Intelligence. [PDF]

  • [ p65] Adnan Darwiche. “An Advance on Variable Elimination with Applications to Tensor-Based Computation”, ECAI 2020. [PDF]

  • [ p64] Adnan Darwiche and Auguste Hirth. “On The Reasons Behind Decisions”, ECAI 2020. [PDF]

  • [ p63] Bolte, F., Nourani, M., Ragan, E., and Bruckner, S. (2020). SplitStreams: A Visual Metaphor for Evolving Hierarchies. IEEE Transactions on Visualization and Computer Graphics (TVCG). pp. 1-15. [paper]

2019

  • [ p62 ] Yatin Nandwani, Abhishek Pathak, Mausam and Parag Singla. “A Primal-Dual Formulation for Deep Learning with Constraints”, Thirty Third International Conference on Neural Information Processing Systems (NeurIPS), 2019. [ PDF]

  • [ p61] Madanagopal, K., Ragan, E., and Benjamin, P. (2019). Analytic Provenance in Practice: The Role of Provenance in Real-World Visualization and Data Analysis Environments. IEEE Computer Graphics and Applications, 39(6), 30-45. [ paper]

  • [ p60] Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari and Guy Van den Broeck. “On Tractable Computation of Expected Predictions”, In Advances in Neural Information Processing Systems 32 (NeurIPS). [PDF]

  • [ p59] Laura I. Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst and Guy Van den Broeck. “Towards Hardware-Aware Tractable Learning of Probabilistic Models”, In Advances in Neural Information Processing Systems 32 (NeurIPS). [PDF]

  • [ p58] Andy Shih, Guy Van den Broeck, Paul Beame and Antoine Amarilli. “Smoothing Structured Decomposable Circuits”, In Advances in Neural Information Processing Systems 32 (NeurIPS). [PDF]

  • [ p57] Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Guy Van den Broeck and Marian Verhelst. “On Hardware-Aware Probabilistic Frameworks for Resource Constrained Embedded Applications”, In Proceedings of the NeurIPS Workshop on Energy Efficient Machine Learning and Cognitive Computing (EMC2). [PDF]

  • [ p56] Zhe Zeng, Fanqi Yan, Paolo Morettin, Antonio Vergari and Guy Van den Broeck. “Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing”, In Proceedings of the NeurIPS Workshop on Knowledge Representation and Reasoning Meets Machine Learning (KR2ML). [PDF]

  • [ p55] Chiradeep Roy, Mahesh Shanbhag, Mahsan Nourani, Tahrima Rahman, Samia Kabir, Vibhav Gogate, Nicholas Ruozzi and Eric Ragan, “Explainable Activity Recognition in Videos using Dynamic Cutset Networks”, IUI 2019 workshop on Explainable Smart Systems (ExSS). [PDF]

  • [ p54] Tahrima Rahman, Shasha Jin and Vibhav Gogate, “Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation”, ICML 2019 [PDF]

  • [ p53] Tahrima Rahman, Shasha Jin and Vibhav Gogate, “Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models”, IJCAI 2019. [PDF]

  • [ p52] Yuqiao Chen, Nicholas Ruozzi, and Sriraam Natarajan. “Lifted Message Passing for Hybrid Probabilistic Inference”, IJCAI 2019. [PDF]

  • [ p51] Yuanzhen Guo, Hao Xiong, Yibo Yang, and Nicholas Ruozzi. “One-Shot Marginal MAP Inference in Markov Random Fields”, UAI 2019. [PDF]

  • [ p50] Shahab Shams, Nicholas Ruozzi, and Peter Csikvari. “Counting Homomorphisms in Bipartite Graphs”, ISIT 2019. [PDF]

  • [ p49] Yibo Yang, Nicholas Ruozzi, and Vibhav Gogate. “Efficient Neural Network Pruning and Quantization by Hard Clustering”, AAAI 2019 Workshop on Network Interpretability. [PDF]

  • [ p48] Yuanzhen Guo, Hao Xiong, and Nicholas Ruozzi. “Marginal Inference in Continuous Markov Random Fields using Mixtures”, AAAI 2019. [PDF]

  • [ p47] Pasha Khosravi, Yitao Liang, YooJung Choi and Guy Van den Broeck. “What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features”, IJCAI 2019. [PDF]

  • [ p46] Tal Friedman and Guy Van den Broeck. “On Constrained Open-World Probabilistic Databases”, IJCAI 2019.[PDF]

  • [ p45] Zhe Zeng and Guy Van den Broeck. “Efficient Search-Based Weighted Model Integration”, UAI 2019.[PDF]

  • [ p44] Steven Holtzen, Todd Millstein and Guy Van den Broeck. “Generating and Sampling Orbits for Lifted Probabilistic Inference”, UAI 2019.[PDF]

  • [ p43] Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari and Guy Van den Broeck. “Tractable Computation of the Moments of Predictive Models”, TPM 2019. [PDF]

  • [ p42] Steven Holtzen, Todd Millstein and Guy Van den Broeck. “Symbolic Exact Inference for Discrete Probabilistic Programs”, TPM 2019. [PDF]

  • [ p41] Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst and Guy Van den Broeck. “Towards Hardware-Aware Tractable Learning of Probabilistic Models”, TPM 2019. [PDF]

  • [ p40] Aishwarya Sivaraman, Tianyi Zhang, Guy Van den Broeck and Miryung Kim. “Active Inductive Logic Programming for Code Search”, ICSE 2019.[PDF]

  • [ p39] Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van den Broeck and Luc De Raedt. “Scalable Rule Learning in Probabilistic Knowledge Bases”, AKBC 2019.[PDF]

  • [ p38] Tal Friedman and Guy Van den Broeck. “On Constrained Open-World Probabilistic Databases”, AKBC 2019.[PDF]

  • [ p37] Nourani, M., Kabir, S., Mohseni, S. and Ragan, E. (accepted). “The Effects of Meaningful and Meaningless Explanations on Trust and Perceived System Accuracy in Intelligent Systems”. HCOMP 2019.

  • [ p36] Yujia Shen and Haiying Huang and Arthur Choi and Adnan Darwiche. “Conditional Independence in Testing Bayesian Networks”, ICML 2019. [PDF]

  • [ p35] Yujia Shen and Anchal Goyanka and Adnan Darwiche and Arthur Choi. “Structured Bayesian Networks: From Inference to Learning with Routes”, AAAI 2019. [PDF]

  • [ p34] Andy Shih and Arthur Choi and Adnan Darwiche. “Compiling Bayesian Networks into Decision Graphs”, AAAI 2019. [PDF]

  • [ p33] Arthur Choi and Weijia Shi and Andy Shih and Adnan Darwiche. “Compiling Neural Networks into Tractable Boolean Circuits”, VNN 2019. [PDF]

  • [ p32] Andy Shih and Adnan Darwiche and Arthur Choi. “Verifying Binarized Neural Networks by Local Automaton Learning”, VNN 2019. [PDF]

  • [ p31] Andy Shih and Adnan Darwiche and Arthur Choi. “Verifying Binarized Neural Networks by Angluin-Style Learning”, SAT 2019. [PDF]

  • [ p30] Happy Mittal, Ayush Bhardwaj, Vibhav Gogate and Parag Singla, “Domain-Size Aware Markov Logic Networks”, AISTATS 2019. [PDF]

2018

  • [ p29] Li Chou, Wolfgang Gatterbauer and Vibhav Gogate, “Dissociation-Based Oblivious Bounds for Weighted Model Counting”, UAI 2018. [PDF]

  • [ p28] Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate and Parag Singla, “Lifted Marginal MAP Inference”, UAI 2018. [PDF]

  • [p27] Sara Rouhani, Tahrima Rahman and Vibhav Gogate, “Algorithms for the Nearest Assignment Problem”, IJCAI 2018. [PDF]

  • [p26] Mahesh Shanbhag, “Activity Recognition in Videos using Deep Learning”, Masters Thesis, UTDallas, 2018. [PDF]

  • [p25] Li Chou, Pracheta Sahoo, Somdeb Sarkhel, Nicholas Ruozzi, and Vibhav Gogate, “Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks,” AAAI 2018. [PDF]

  • [p24] Tal Friedman and Guy Van den Broeck. “Approximate Knowledge Compilation by Online Collapsed Importance Sampling”, NeurIPS, 2018. [PDF]

  • [p23] Yitao Liang and Guy Van den Broeck. “Learning Logistic Circuits”, UAI 2018 Workshop on Uncertainty in Deep Learning, 2018. [PDF]

  • [p22] Steven Holtzen, Guy Van den Broeck and Todd Millstein, ”Sound Abstraction and Decomposition of Probabilistic Programs,” ICML 2018. [PDF]

  • [p21] Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang and Guy Van den Broeck, “A Semantic Loss Function for Deep Learning with Symbolic Knowledge,” ICML 2018. [PDF]

  • [p20] YooJung Choi and Guy Van den Broeck, “On Robust Trimming of Bayesian Network Classifiers,” IJCAI 2018. [PDF]

  • [ p19] Andy Shih, Arthur Choi and Adnan Darwiche, “Formal Verification of Bayesian Network Classifiers”, PGM 2018. [PDF]

  • [ p18] Arthur Choi and Adnan Darwiche, “On the Relative Expressiveness of Bayesian and Neural Networks”, PGM 2018. [PDF]

  • [p17] Arthur Choi and Yujia Shen and Adnan Darwiche, “Tractability in Structured Probability Spaces,” NIPS 2017. [PDF]

  • [p16] Andy Shih and Arthur Choi and Adnan Darwiche, “A Symbolic Approach to Explaining Bayesian Network Classifiers”, IJCAI 2018. [PDF]

  • [p15] Yujia Shen and Arthur Choi and Adnan Darwiche,” Conditional PSDDs: Modeling and Learning with Modular Knowledge”, AAAI 2018. [PDF]

  • [p14] Eunice Yuh-Jie Chen and Adnan Darwiche and Arthur Choi, “On Pruning with MDL score,” IJAR journal, 2018. [PDF]

  • [p13] Gagan Madan, Ankit Anand, Mausam and Parag Singla. “Block-Value Symmetries in Probabilistic Grpahical Models”, UAI 2018. [PDF]

  • [p12] Dinesh Khandelwal, Parag Singla and Chetan Arora. “Learning Higher Order Potentials for MRFs”, WACV 2018. [PDF]

2017

  • [p11] Arthur Choi and Adnan Darwiche, “On Relaxing Determinism in Arithmetic Circuits,” ICML 2017. [PDF]

  • [p10] YooJung Choi, Adnan Darwiche, and Guy Van den Broeck. “Optimal Feature Selection for Decision Robustness in Bayesian Networks,” IJCAI 2017. [PDF]

  • [p9] Yujia Shen and Arthur Choi and Adnan Darwiche, “A Tractable Probabilistic Model for Subset Selection,” UAI 2017. [PDF]

  • [p8] Yitao Liang and Guy Van den Broeck, “Towards Compact Interpretable Models: Shrinking of Learned Probabilistic Sentential Decision Diagrams,” In IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), 2017. [PDF]

  • [p7] Yitao Liang, Jessa Bekker and Guy Van den Broeck, “Learning the Structure of Probabilistic Sentential Decision Diagrams,” UAI 2017. [PDF]

  • [p6] Steven Holtzen, Todd Millstein and Guy Van den Broeck, “Probabilistic Program Abstractions,” UAI 2017. [PDF]

  • [p5] Shahroze Kabir, Frederic Sala, Guy Van den Broeck and Lara Dolecek, “Coded Machine Learning: Joint Informed Replication and Learning for Linear Regression,” Allerton Conference on Communication, Control, and Computing, 2017. [PDF]

  • [p4] Haroun Habeeb and Ankit Anand, Mausam and Parag Singla, "Coarse-to-fine Lifted MAP inference in Computer Vision,” IJCAI 2017. [PDF]

  • [p3] Gregory Van Buskirk, Benjamin Raichel, and Nicholas Ruozzi, “Sparse Approximate Conic Hulls,” NIPS 2017. [PDF]

  • [p2] Tahrima Rahman, “Scalable Learning Approaches for Sum-product-cutset networks”, Ph.D. Thesis, 2017. [PDF]

  • [p1] David Smith, Sara Rouhani, and Vibhav Gogate, “Order Statistics for Probabilistic Graphical Models,” IJCAI 2017. [PDF]