|
| [198] | Honghua Zhang, Liunian Harold Li, Tao Meng, Kai-Wei Chang and Guy Van den Broeck. On the Paradox of Learning to Reason from Data, In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI), 2023. |
| [197] | Zhe Zeng and Guy Van den Broeck. Collapsed Inference for Bayesian Deep Learning, In ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling, 2023. |
| [196] | Kareem Ahmed, Kai-Wei Chang and Guy Van den Broeck. A Pseudo-Semantic Loss for Deep Generative Models with Logical Constraints, In Knowledge and Logical Reasoning in the Era of Data-driven Learning Workshop, 2023. |
| [195] | Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert and Christopher Morris. Probabilistic Task-Adaptive Graph Rewiring, In ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators, 2023. |
| [194] | Vinay Shukla, Zhe Zeng, Kareem Ahmed and Guy Van den Broeck. A Unified Approach to Count-Based Weakly-Supervised Learning, In ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators, 2023. |
| [193] | William X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen, Todd Millstein and Guy Van den Broeck. Scaling Integer Arithmetic in Probabilistic Programs, In Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2023. |
| [192] | Honghua Zhang, Meihua Dang, Nanyun Peng and Guy Van den Broeck. Tractable Control for Autoregressive Language Generation, In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023. Oral full presentation, acceptance rate 155/6538 = 2.4% |
| [191] | Xuejie Liu, Anji Liu, Guy Van den Broeck and Yitao Liang. Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits, In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023. |
| [190] | Yizhuo Chen, Kaizhao Liang, Zhe Zeng, Yifei Yang, Shuochao Yao and Huajie Shao. A Unified Knowledge Distillation Framework for Deep Directed Graphical Models, In 2023 Conference on Computer Vision and Pattern Recognition (CVPR), 2023. |
| [189] | Anji Liu, Honghua Zhang and Guy Van den Broeck. Scaling Up Probabilistic Circuits by Latent Variable Distillation, In Proceedings of the International Conference on Learning Representations (ICLR), 2023. Oral full presentation, acceptance rate 90/4849 = 1.8% |
| [188] | Kareem Ahmed, Zhe Zeng, Mathias Niepert and Guy Van den Broeck. SIMPLE: A Gradient Estimator for k-subset sampling, In Proceedings of the International Conference on Learning Representations (ICLR), 2023. |
| [187] | Nikil Roashan Selvam, Honghua Zhang and Guy Van den Broeck. Mixtures of All Trees, In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. |
| [186] | Kareem Ahmed, Kai-Wei Chang and Guy Van den Broeck. Semantic Strengthening of Neuro-Symbolic Learning, In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. |
| [185] | Anji Liu, Hongming Xu, Guy Van den Broeck and Yitao Liang. Out-of-Distribution Generalization by Neural-Symbolic Joint Training, In Proceedings of the 37th AAAI Conference on Artificial Intelligence, 2023. |
| [184] | Nikil Roashan Selvam, Guy Van den Broeck and YooJung Choi. Certifying Fairness of Probabilistic Circuits, In Proceedings of the 37th AAAI Conference on Artificial Intelligence, 2023. |
| [183] | Poorva Garg, Steven Holtzen, Guy Van den Broeck and Todd Millstein. Bit Blasting Probabilistic Programs, In POPL 2023 Language for Inference Workshop, 2023. |
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| [182] | Meihua Dang, Anji Liu and Guy Van den Broeck. Sparse Probabilistic Circuits via Pruning and Growing, In Advances in Neural Information Processing Systems 35 (NeurIPS), 2022. Oral full presentation, acceptance rate 201/10411 = 1.9% |
| [181] | Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck and Antonio Vergari. Semantic Probabilistic Layers for Neuro-Symbolic Learning, In Advances in Neural Information Processing Systems 35 (NeurIPS), 2022. |
| [180] | Kareem Ahmed, Eric Wang, Kai-Wei Chang and Guy Van den Broeck. Neuro-Symbolic Entropy Regularization, In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022. Oral full presentation, acceptance rate 36/712 = 5% |
| [179] | Pasha Khosravi, Antonio Vergari and Guy Van den Broeck. Why Is This an Outlier? Explaining Outliers by Submodular Optimization of Marginal Distributions, In Proceedings of the UAI Workshop on Tractable Probabilistic Modeling (TPM), 2022. |
| [178] | Zhe Zeng and Guy Van den Broeck. Collapsed Inference for Bayesian Deep Learning, In Proceedings of the ICML Workshop on Beyond Bayes: Paths Towards Universal Reasoning Systems, 2022. |
| [177] | Meihua Dang, Anji Liu, Xinzhu Wei, Sriram Sankararaman and Guy Van den Broeck. Tractable and Expressive Generative Models of Genetic Variation Data, In Proceedings of the International Conference on Research in Computational Molecular Biology (RECOMB), 2022. |
| [176] | Anji Liu, Stephan Mandt and Guy Van den Broeck. Lossless Compression with Probabilistic Circuits, In Proceedings of the International Conference on Learning Representations (ICLR), 2022. Oral spotlight presentation, acceptance rate 176/3391 = 5.2% |
| [175] | YooJung Choi, Tal Friedman and Guy Van den Broeck. Solving Marginal MAP Exactly by Probabilistic Circuit Transformations, In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. |
| [174] | Kareem Ahmed, Tao Li, Thy Ton, Quan Guo, Kai-Wei Chang, Parisa Kordjamshidi, Vivek Srikumar, Guy Van den Broeck and Sameer Singh. PYLON: A PyTorch Framework for Learning with Constraints, In Proceedings of the 36th AAAI Conference on Artificial Intelligence (Demo Track), 2022. |
| [173] | Meihua Dang, Antonio Vergari and Guy Van den Broeck. Strudel: A Fast and Accurate Learner of Structured-Decomposable Probabilistic Circuits, In International Journal of Approximate Reasoning, volume 140, 2022. |
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| [172] | Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso and Guy Van den Broeck. A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference, In Advances in Neural Information Processing Systems 34 (NeurIPS), 2021. Oral full presentation, acceptance rate 55/9122 = 0.6% |
| [171] | Anji Liu and Guy Van den Broeck. Tractable Regularization of Probabilistic Circuits, In Advances in Neural Information Processing Systems 34 (NeurIPS), 2021. Oral spotlight presentation, acceptance rate 340/9122 = 3.7% |
| [170] | Yu-Hsi Cheng, Todd Millstein, Guy Van den Broeck and Steven Holtzen. flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs, In International Conference on Probabilistic Programming (PROBPROG), 2021. |
| [169] | Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan and David Poole. An Introduction to Lifted Probabilistic Inference, MIT Press, 2021. |
| [168] | Rushil Gupta, Vishal Sharma, Yash Jain, Yitao Liang, Guy Van den Broeck and Parag Singla. Towards an Interpretable Latent Space in Structured Models for Video Prediction, In IJCAI 2021 Weakly Supervised Representation Learning Workshop (WSRL), 2021. |
| [167] | Eric Wang, Pasha Khosravi and Guy Van den Broeck. Probabilistic Sufficient Explanations, In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021. |
| [166] | Steven Holtzen, Sebastian Junges, Marcell Vazquez-Chanlatte, Todd Millstein, Sanjit A. Seshia and Guy Van den Broeck. Model Checking Finite-Horizon Markov Chains with Probabilistic Inference, In Proceedings of the 33rd International Conference on Computer-Aided Verification (CAV), 2021. |
| [165] | Honghua Zhang, Brendan Juba and Guy Van den Broeck. Probabilistic Generating Circuits, In Proceedings of the UAI Workshop on Tractable Probabilistic Modeling (TPM), 2021. TPM best paper award |
| [164] | Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari and Guy Van den Broeck. Is Parameter Learning via Weighted Model Integration Tractable?, In Proceedings of the UAI Workshop on Tractable Probabilistic Modeling (TPM), 2021. |
| [163] | Wenzhe Li, Zhe Zeng, Antonio Vergari and Guy Van den Broeck. Tractable Computation of Expected Kernels, In Proceedings of the 37th Conference on Uncertainty in Aritifical Intelligence (UAI), 2021. |
| [162] | Honghua Zhang, Brendan Juba and Guy Van den Broeck. Probabilistic Generating Circuits, In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021. Long presentation, acceptance rate 166/5513 = 3% |
| [161] | Steven Holtzen. Exploiting Program Structure for Scaling Probabilistic Programming, PhD thesis, University of California, Los Angeles, 2021. UCLA Computer Science Outstanding Graduating PhD Student Award |
| [160] | Kareem Ahmed, Eric Wang, Kai-Wei Chang and Guy Van den Broeck. Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction, 2021. |
| [159] | Ismail Ilkan Ceylan, Adnan Darwiche and Guy Van den Broeck. Open-World Probabilistic Databases: Semantics, Algorithms, Complexity, In Artificial Intelligence, 2021. |
| [158] | Guy Van den Broeck, Anton Lykov, Maximilian Schleich and Dan Suciu. On the Tractability of SHAP Explanations, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. AAAI distinguished paper award |
| [157] | YooJung Choi, Meihua Dang and Guy Van den Broeck. Group Fairness by Probabilistic Modeling with Latent Fair Decisions, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. |
| [156] | Meihua Dang, Pasha Khosravi, Yitao Liang, Antonio Vergari and Guy Van den Broeck. Juice: A Julia Package for Logic and Probabilistic Circuits, In Proceedings of the 35th AAAI Conference on Artificial Intelligence (Demo Track), 2021. |
| [155] | Yipeng Huang, Steven Holtzen, Todd Millstein, Guy Van den Broeck and Margaret R. Martonosi. Logical Abstractions for Noisy Variational Quantum Algorithm Simulation, In Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2021. IEEE Micro top picks 2022 honorable mention |
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| [154] | Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein and Guy Van den Broeck. Counterexample-Guided Learning of Monotonic Neural Networks, In Advances in Neural Information Processing Systems 33 (NeurIPS), 2020. |
| [153] | Anji Liu, Yitao Liang, Ji Liu, Guy Van den Broeck and Jianshu Chen. On Effective Parallelization of Monte Carlo Tree Search, In Deep Reinforcement Learning Workshop at NeurIPS (DRLW), 2020. |
| [152] | Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari and Guy Van den Broeck. Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations, In Advances in Neural Information Processing Systems 33 (NeurIPS), 2020. Oral spotlight presentation, acceptance rate 385/9454 = 4.1% |
| [151] | Albert Zhao, Tong He, Yitao Liang, Haibin Huang, Guy Van den Broeck and Stefano Soatto. SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning, In Conference on Robot Learning, 2020. |
| [150] | Steven Holtzen, Guy Van den Broeck and Todd Millstein. Scaling Exact Inference for Discrete Probabilistic Programs, In Proc. ACM Program. Lang. (OOPSLA), ACM, 2020. ACM SIGPLAN distinguished paper award |
| [149] | YooJung Choi, Antonio Vergari and Guy Van den Broeck. Probabilistic Circuits: A Unifying Framework for Tractable Probabilistic Models, In , 2020. |
| [148] | Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari and Guy Van den Broeck. Relax, compensate and then integrate, In Proceedings of the ECML-PKDD Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML), 2020. |
| [147] | Meihua Dang, Antonio Vergari and Guy Van den Broeck. Strudel: Learning Structured-Decomposable Probabilistic Circuits, In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM), 2020. |
| [146] | Honghua Zhang, Steven Holtzen and Guy Van den Broeck. On the Relationship Between Probabilistic Circuits and Determinantal Point Processes, In Proceedings of the 36th Conference on Uncertainty in Aritifical Intelligence (UAI), 2020. |
| [145] | Tal Friedman and Guy Van den Broeck. Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings, In Proceedings of the 36th Conference on Uncertainty in Aritifical Intelligence (UAI), 2020. |
| [144] | Eric Wang, Pasha Khosravi and Guy Van den Broeck. Towards Probabilistic Sufficient Explanations, In Extending Explainable AI Beyond Deep Models and Classifiers Workshop at ICML (XXAI), 2020. |
| [143] | Pasha Khosravi, Antonio Vergari, YooJung Choi, Yitao Liang and Guy Van den Broeck. Handling Missing Data in Decision Trees: A Probabilistic Approach, In The Art of Learning with Missing Values Workshop at ICML (Artemiss), 2020. |
| [142] | Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari and Guy Van den Broeck. Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing, In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020. |
| [141] | Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting and Zoubin Ghahramani. Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits, In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020. |
| [140] | Guy Van den Broeck, Anton Lykov, Maximilian Schleich and Dan Suciu. On the Tractability of SHAP Explanations, In Journal of Artificial Intelligence Research (JAIR), AI Access Foundation, 2020. |
| [139] | 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), 2020. |
| [138] | Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black and Bernhard Schölkopf. From Variational to Deterministic Autoencoders, In Proceedings of the 8th International Conference on Learning Representations (ICLR), 2020. |
| [137] | 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), 2020. |
| [136] | Anji Liu, Jianshu Chen, Mingze Yu, Yu Zhai, Xuewen Zhou and Ji Liu. Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search, In Proceedings of the 8th International Conference on Learning Representations (ICLR), 2020. Oral full presentation, acceptance rate 48/2594 = 1.9% |
| [135] | Krzysztof Gajowniczek, Yitao Liang, Tal Friedman, Tomasz Ząbkowski and Guy Van den Broeck. Semantic and Generalized Entropy Loss Functions for Semi-Supervised Deep Learning, In Entropy, volume 22, 2020. |
| [134] | 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), 2020. StarAI best paper award |
| [133] | 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, 2020. |
| [132] | YooJung Choi, Antonio Vergari and Guy Van den Broeck. Lecture Notes: Probabilistic Circuits: Representation and Inference, In , 2020. |
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| [131] | 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), 2019. |
| [130] | 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), 2019. |
| [129] | Andy Shih, Guy Van den Broeck, Paul Beame and Antoine Amarilli. Smoothing Structured Decomposable Circuits, In Advances in Neural Information Processing Systems 32 (NeurIPS), 2019. Oral spotlight presentation, acceptance rate 164/6743 = 2.4% |
| [128] | 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), 2019. |
| [127] | 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), 2019. |
| [126] | Alicia Solow-Niederman, YooJung Choi and Guy Van den Broeck. The Institutional Life of Algorithmic Risk Assessment, In Berkeley Technology Law Journal, 2019. |
| [125] | Pasha Khosravi, Yitao Liang, YooJung Choi and Guy Van den Broeck. What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features, In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019. |
| [124] | Tal Friedman and Guy Van den Broeck. On Constrained Open-World Probabilistic Databases, In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019. |
| [123] | Zhe Zeng and Guy Van den Broeck. Efficient Search-Based Weighted Model Integration, In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), 2019. |
| [122] | Steven Holtzen, Todd Millstein and Guy Van den Broeck. Generating and Sampling Orbits for Lifted Probabilistic Inference, In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), 2019. Oral full presentation, acceptance rate 35/450 = 7% |
| [121] | Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Xiaoting Shao, Martin Trapp, Kristian Kersting and Zoubin Ghahramani. Random Sum-Product Networks: A Simple but Effective Approach to Probabilistic Deep Learning, In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), 2019. Oral full presentation |
| [120] | Steven Holtzen, Todd Millstein and Guy Van den Broeck. Symbolic Exact Inference for Discrete Probabilistic Programs, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), 2019. |
| [119] | Aishwarya Sivaraman, Tianyi Zhang, Guy Van den Broeck and Miryung Kim. Active Inductive Logic Programming for Code Search, In The 41st ACM/IEEE International Conference on Software Engineering (ICSE), 2019. |
| [118] | Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van den Broeck and Luc De Raedt. Scalable Rule Learning in Probabilistic Knowledge Bases, In The 1st Conference On Automated Knowledge Base Construction (AKBC), 2019. |
| [117] | Tal Friedman and Guy Van den Broeck. On Constrained Open-World Probabilistic Databases, In The 1st Conference On Automated Knowledge Base Construction (AKBC), 2019. |
| [116] | Alicia Solow-Niederman, Guy Van den Broeck and YooJung Choi. The Institutional Life of Algorithms: Lessons from California's Money Bail Reform Act, In The 8th Annual Conference On Robotics, Law & Policy, 2019. |
| [115] | Yitao Liang and Guy Van den Broeck. Learning Logistic Circuits, In Proceedings of the 33rd Conference on Artificial Intelligence (AAAI), 2019. Oral full presentation, acceptance rate 460/7700 = 6% |
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| [114] | Zehong Hu, Yitao Liang, Jie Zhang, Zhao Li and Yang Liu. Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing, In Advances in Neural Information Processing Systems 31 (NeurIPS), 2018. |
| [113] | Tal Friedman and Guy Van den Broeck. Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In Advances in Neural Information Processing Systems 31 (NeurIPS), 2018. Oral full presentation, acceptance rate 30/4856 = 0.6% |
| [112] | Steven Holtzen, Guy Van den Broeck and Todd Millstein. Sound Abstraction and Decomposition of Probabilistic Programs, In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018. |
| [111] | Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang and Guy Van den Broeck. A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018. |
| [110] | YooJung Choi and Guy Van den Broeck. On Robust Trimming of Bayesian Network Classifiers, In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018. |
| [109] | Steven Holtzen, Guy Van den Broeck and Todd Millstein. Probabilistic Program Inference With Abstractions, In POPL 2018 Probabilistic Programming Languages, Semantics, and Systems Workshop, 2018. |
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| [108] | Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang and Guy Van den Broeck. A Semantic Loss Function for Deep Learning Under Weak Supervision, In NIPS 2017 Workshop on Learning with Limited Labeled Data: Weak Supervision and Beyond, 2017. LLD best paper award runner up |
| [107] | Shahroze Kabir, Frederic Sala, Guy Van den Broeck and Lara Dolecek. Coded Machine Learning: Joint Informed Replication and Learning for Linear Regression, In Proceedings of the 55th Annual Allerton Conference on Communication, Control, and Computing, 2017. |
| [106] | 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. |
| [105] | Guy Van den Broeck and Dan Suciu. Query Processing on Probabilistic Data: A Survey, Foundations and Trends in Databases, Now Publishers, 2017. |
| [104] | Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck and David Poole. Domain Recursion for Lifted Inference with Existential Quantifiers, In Seventh International Workshop on Statistical Relational AI (StarAI), 2017. |
| [103] | Yitao Liang, Jessa Bekker and Guy Van den Broeck. Learning the Structure of Probabilistic Sentential Decision Diagrams, In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 2017. Oral full presentation, acceptance rate 29/289 = 10% |
| [102] | Anna Latour, Behrouz Babaki, Anton Dries, Angelika Kimmig, Guy Van den Broeck and Siegfried Nijssen. Combining Stochastic Constraint Optimization and Probabilistic Programming: From Knowledge Compilation to Constraint Solving, In Proceedings of the 23rd International Conference on Principles and Practice of Constraint Programming (CP), 2017. |
| [101] | YooJung Choi, Adnan Darwiche and Guy Van den Broeck. Optimal Feature Selection for Decision Robustness in Bayesian Networks, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017. |
| [100] | Frederic Sala, Shahroze Kabir, Lara Dolecek and Guy Van den Broeck. Don’t Fear the Bit Flips: Robust Linear Prediction Through Informed Channel Coding, In ICML 2017 Workshop on Reliable Machine Learning in the Wild, 2017. |
| [99] | Ismail Ilkan Ceylan, Adnan Darwiche and Guy Van den Broeck. Open-World Probabilistic Databases: An Abridged Report, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), Sister Conference Best Paper Track, 2017. |
| [98] | Steven Holtzen, Todd Millstein and Guy Van den Broeck. Probabilistic Program Abstractions, In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 2017. |
| [97] | Frederic Sala, Shahroze Kabir, Guy Van den Broeck and Lara Dolecek. Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification, In CoRR, volume abs/1703.02641, 2017. |
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| [96] | Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck and David Poole. New Liftable Classes for First-Order Probabilistic Inference, In Advances in Neural Information Processing Systems 29 (NIPS), 2016. |
| [95] | Angelika Kimmig, Guy Van den Broeck and Luc De Raedt. Algebraic Model Counting, In International Journal of Applied Logic, 2016. |
| [94] | Kayvon Mazooji, Frederic Sala, Guy Van den Broeck and Lara Dolecek. Robust Channel Coding Strategies for Machine Learning Data, In Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing, 2016. |
| [93] | Vaishak Belle, Guy Van den Broeck and Andrea Passerini. Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report, In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), Sister Conference Best Paper Track, 2016. |
| [92] | Guy Van den Broeck. First-Order Model Counting in a Nutshell, In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), Early Career Spotlight Track, 2016. |
| [91] | Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert and Luc De Raedt. Tp-Compilation for Inference in Probabilistic Logic Programs, In International Journal of Approximate Reasoning, 2016. |
| [90] | Ismail Ilkan Ceylan, Adnan Darwiche and Guy Van den Broeck. Open-World Probabilistic Databases, In Proceedings of the 15th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2016. KR best student paper award |
| [89] | Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck and Luc De Raedt. Exploiting Local and Repeated Structure in Dynamic Bayesian Networks, In Artificial Intelligence, volume 232, 2016. |
| [88] | Vaishak Belle, Guy Van den Broeck and Andrea Passerini. Component Caching in Hybrid Domains with Piecewise Polynomial Densities, In Proceedings of the 30th Conference on Artificial Intelligence (AAAI), 2016. |
| [87] | Wannes Meert, Jonas Vlasselaer and Guy Van den Broeck. A Relaxed Tseitin Transformation for Weighted Model Counting, In International Workshop on Statistical Relational AI, 2016. |
| [86] | Babak Salimi, Leopoldo Bertossi, Dan Suciu and Guy Van den Broeck. Quantifying Causal Effects on Query Answering in Databases, In 8th USENIX Workshop on the Theory and Practice of Provenance (TaPP), USENIX Association, 2016. |
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| [85] | Jessa Bekker, Jesse Davis, Arthur Choi, Adnan Darwiche and Guy Van den Broeck. Tractable Learning for Complex Probability Queries, In Advances in Neural Information Processing Systems 28 (NIPS), 2015. |
| [84] | Daan Fierens, Guy Van den Broeck, Joris Renkens, Dimitar Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens and Luc De Raedt. Inference and Learning in Probabilistic Logic Programs using Weighted Boolean Formulas, In Theory and Practice of Logic Programming, volume 15, 2015. |
| [83] | Bart Bogaerts and Guy Van den Broeck. Knowledge Compilation of Logic Programs Using Approximation Fixpoint Theory, In Theory and Practice of Logic Programming, volume 15, 2015. |
| [82] | Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert and Luc De Raedt. Anytime Inference in Probabilistic Logic Programs with Tp-compilation, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. |
| [81] | Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck and Mathias Verbeke. Inducing Probabilistic Relational Rules from Probabilistic Examples, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. |
| [80] | Guy Van den Broeck, Karthika Mohan, Arthur Choi, Adnan Darwiche and Judea Pearl. Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data, In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015. Oral full presentation, acceptance rate 28/292 = 9% |
| [79] | Guy Van den Broeck. Towards High-Level Probabilistic Reasoning with Lifted Inference, In Proceedings of the AAAI Spring Symposium on KRR, 2015. |
| [78] | Guy Van den Broeck and Adnan Darwiche. On the Role of Canonicity in Knowledge Compilation, In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), 2015. |
| [77] | Guy Van den Broeck and Mathias Niepert. Lifted Probabilistic Inference for Asymmetric Graphical Models, In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), 2015. |
| [76] | Jan Van Haaren, Guy Van den Broeck, Wannes Meert and Jesse Davis. Lifted Generative Learning of Markov Logic Networks, In Machine Learning, volume 103, 2015. |
| [75] | Wannes Meert, Vincent Nys, Robin Theunis, Thomas Fannes, Monique Ingels, Tias Guns, Guy Van den Broeck, Kurt Driessens, Danny De Schreye and Marian Verhelst. Innovation Lab @ KU Leuven: Education, Engineering and Artificial Intelligence, In , 2015. |
| [74] | Anton Dries, Angelika Kimmig, Wannes Meert, Joris Renkens, Guy Van den Broeck, Jonas Vlasselaer and Luc De Raedt. ProbLog2: Probabilistic logic programming, In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Demo Track, 2015. |
| [73] | Arthur Choi, Guy Van den Broeck and Adnan Darwiche. Probability Distributions over Structured Spaces, In Proceedings of the AAAI Spring Symposium on KRR, 2015. |
| [72] | Arthur Choi, Guy Van den Broeck and Adnan Darwiche. Tractable Learning for Structured Probability Spaces: A Case Study in Learning Preference Distributions, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. |
| [71] | Vaishak Belle, Guy Van den Broeck and Andrea Passerini. Hashing-Based Approximate Probabilistic Inference in Hybrid Domains, In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015. UAI best paper award |
| [70] | Vaishak Belle, Andrea Passerini and Guy Van den Broeck. Probabilistic Inference in Hybrid Domains by Weighted Model Integration, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. |
| [69] | Paul Beame, Guy Van den Broeck, Eric Gribkoff and Dan Suciu. Symmetric Weighted First-Order Model Counting, In Proceedings of the 34th ACM Symposium on Principles of Database Systems (PODS), 2015. |
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| [68] | Eric Gribkoff, Dan Suciu and Guy Van den Broeck. Lifted probabilistic inference: A guide for the database researcher, In Bulletin of the Technical Committee on Data Engineering, volume 37, 2014. |
| [67] | Jonas Vlasselaer, Joris Renkens, Guy Van den Broeck and Luc De Raedt. Compiling probabilistic logic programs into sentential decision diagrams, In Workshop on Probabilistic Logic Programming (PLP), 2014. |
| [66] | Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck and Luc De Raedt. Efficient probabilistic inference for dynamic relational models, 2014. |
| [65] | Wannes Meert, Guy Van den Broeck and Adnan Darwiche. Lifted inference for probabilistic logic programs, In Workshop on Probabilistic Logic Programming (PLP), 2014. |
| [64] | Guy Van den Broeck, Wannes Meert and Adnan Darwiche. Skolemization for weighted first-order model counting, In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2014. |
| [63] | Mathias Niepert and Guy Van den Broeck. Tractability through exchangeability: A new perspective on efficient probabilistic inference, In Proceedings of the 28th AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, 2014. AAAI best paper honorable mention |
| [62] | Eric Gribkoff, Guy Van den Broeck and Dan Suciu. Understanding the complexity of lifted inference and asymmetric weighted model counting, In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI), 2014. |
| [61] | Doga Kisa, Guy Van den Broeck, Arthur Choi and Adnan Darwiche. Probabilistic sentential decision diagrams, In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2014. |
| [60] | Eric Gribkoff, Guy Van den Broeck and Dan Suciu. The most probable database problem, In Proceedings of the First International Workshop on Big Uncertain Data (BUDA), 2014. |
| [59] | Doga Kisa, Guy Van den Broeck, Arthur Choi and Adnan Darwiche. Probabilistic sentential decision diagrams: Learning with massive logical constraints, In ICML Workshop on Learning Tractable Probabilistic Models (LTPM), 2014. |
| [58] | Jan Van Haaren, Guy Van den Broeck, Wannes Meert and Jesse Davis. Tractable learning of liftable Markov logic networks, In Proceedings of the ICML-14 Workshop on Learning Tractable Probabilistic Models (LTPM), 2014. |
| [57] | Joris Renkens, Angelika Kimmig, Guy Van den Broeck and Luc De Raedt. Explanation-based approximate weighted model counting for probabilistic logics, In Proceedings of the 28th AAAI Conference on Artificial Intelligence, AAAI, 2014. |
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| [56] | Guy Van den Broeck and Adnan Darwiche. On the complexity and approximation of binary evidence in lifted inference, In Advances in Neural Information Processing Systems 26 (NIPS), 2013. Oral spotlight presentation, acceptance rate 72/1420 = 5% |
| [55] | Jan Van Haaren, Albrecht Zimmermann, Joris Renkens, Guy Van den Broeck, Tim Op De Beéck, Wannes Meert and Jesse Davis. Machine learning and data mining for sports analytics, 2013. |
| [54] | Guy Van den Broeck, Wannes Meert and Jesse Davis. Lifted generative parameter learning, In Statistical Relational AI (StaRAI) workshop, 2013. |
| [53] | Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis and Hendrik Blockeel. On the completeness of lifted variable elimination, In International Workshop on Statistical Relational AI (StarAI-13), Bellevue, Washington, 15 July 2013, 2013. |
| [52] | Guy Van den Broeck. On the complexity and approximation of binary evidence for lifted inference, In Proceedings of StaRAI, Statistical Relational AI workshop, Bellevue, Washington, USA, 2013. |
| [51] | Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis and Hendrik Blockeel. Completeness results for lifted variable elimination, In Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR Workshop and Conference Proceedings (Carlos M. Carvalho, Pradeep Ravikumar, eds.), 2013. |
| [50] | Guy Van den Broeck. Lifted Inference and Learning in Statistical Relational Models, PhD thesis, KU Leuven, 2013. ECCAI Artificial Intelligence Dissertation Award Scientific prize IBM Belgium for Informatics |
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| [49] | Joris Renkens, Dimitar Shterionov, Guy Van den Broeck, Jonas Vlasselaer, Daan Fierens, Wannes Meert, Gerda Janssens and Luc De Raedt. ProbLog2: From probabilistic programming to statistical relational learning, In Proceedings of the NIPS Probabilistic Programming Workshop, (Daniel Roy, Vikash Mansinghka, Noah Goodman, eds.), 2012. |
| [48] | Wannes Meert, Guy Van den Broeck, Nima Taghipour, Daan Fierens, Hendrik Blockeel, Jesse Davis and Luc De Raedt. Lifted inference for probabilistic programming, In Proceedings of the NIPS Probabilistic Programming Workshop,, 2012. |
| [47] | Daan Fierens, Guy Van den Broeck, Maurice Bruynooghe and Luc De Raedt. Constraints for probabilistic logic programming, In Proceedings of the NIPS Probabilistic Programming Workshop, (Daniel Roy, Vikash Mansinghka, Noah Goodman, eds.), 2012. |
| [46] | Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis and Hendrik Blockeel. Lifted Variable Elimination: A Novel Operator and Completeness Results, In ArXiv e-prints, 2012. |
| [45] | Joris Renkens, Guy Van den Broeck and Siegfried Nijssen. k-optimal: A novel approximate inference algorithm for ProbLog, In Machine Learning, volume 89, 2012. ILP best student paper award |
| [44] | Guy Van den Broeck, Arthur Choi and Adnan Darwiche. Lifted relax, compensate and then recover: From approximate to exact lifted probabilistic inference, In Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI) (Nando de Freitas, Kevin Murphy, eds.), 2012. |
| [43] | Guy Van den Broeck and Jesse Davis. Conditioning in first-order knowledge compilation and lifted probabilistic inference, In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, (Joerg Hoffmann, Bart Selman, eds.), AAAI Press, 2012. |
| [42] | Jan Van Haaren and Guy Van den Broeck. Relational learning for football-related predictions, In Proceedings of the 21st Belgian-Dutch Conference on Machine Learning, 2012. |
| [41] | Angelika Kimmig, Guy Van den Broeck and Luc De Raedt. Algebraic Model Counting, In CoRR, volume abs/1211.4475, 2012. |
| [40] | Manfred Jaeger and Guy Van den Broeck. Liftability of probabilistic inference: Upper and lower bounds, In Proceedings of the 2nd International Workshop on Statistical Relational AI,, 2012. |
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| [39] | Guy Van den Broeck. On the completeness of first-order knowledge compilation for lifted probabilistic inference, In Advances in Neural Information Processing Systems 24 (NIPS),, 2011. Oral full presentation, acceptance rate 20/1400 = 1.4% |
| [38] | Guy Van den Broeck and Kurt Driessens. Automatic discretization of actions and states in Monte-Carlo tree search, In Proceedings of the ECML/PKDD 2011 Workshop on Machine Learning and Data Mining in and around Games, (Tom Croonenborghs, Kurt Driessens, Olana Missura, eds.), 2011. |
| [37] | Guy Van den Broeck, Nima Taghipour, Wannes Meert, Jesse Davis and Luc De Raedt. Lifted probabilistic inference by first-order knowledge compilation, In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI) (Toby Walsh, ed.), AAAI Press/International Joint Conferences on Artificial Intelligence, 2011. |
| [36] | Jan Van Haaren and Guy Van den Broeck. Relational learning for football-related predictions, In Preliminary Papers ILP, 2011. |
| [35] | Joris Renkens, Guy Van den Broeck and Siegfried Nijssen. k-Optimal: A novel approximate inference algorithm for ProbLog, In Preliminary Papers ILP, 2011. |
| [34] | Davide Nitti, Guy Van den Broeck and Luc De Raedt. Probabilistic logic in dynamic domains: Particle filter with distributional clauses, In Preliminary Papers ILP, 2011. |
| [33] | Angelika Kimmig, Bernd Gutmann, Theofrastos Mantadelis, Guy Van den Broeck, Vitor Santos Costa, Gerda Janssens and Luc De Raedt. ProbLog, Association for Logic Programming, 2011. |
| [32] | Angelika Kimmig, Guy Van den Broeck and Luc De Raedt. An algebraic Prolog for reasoning about possible worlds, In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, (Wolfram Burgard, Dan Roth, eds.), AAAI Press, 2011. |
| [31] | Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann and Luc De Raedt. Inference in probabilistic logic programs using weighted CNF's, In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), (Fabio Gagliardi Cozman, Avi Pfeffer, eds.), 2011. Oral full presentation, acceptance rate 24/285 = 8% |
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| [30] | Guy Van den Broeck, Ingo Thon, Martijn van Otterlo and Luc De Raedt. DTProbLog: A decision-theoretic probabilistic Prolog, In Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence, (Maria Fox, David Poole, eds.), AAAI Press, 2010. |
| [29] | Ingo Thon, Bernd Gutmann and Guy Van den Broeck. Probabilistic programming for planning problems, In Statistical Relational AI workshop (Kristian Kersting, Stuart Russell, Leslie Pack Kaelbling, Alon Halevy, Sriraam Natarajan, Lilyana Mihalkova, eds.), 2010. |
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| [28] | Guy Van den Broeck, Kurt Driessens and Jan Ramon. Monte-Carlo tree search in poker using expected reward distributions, In Proceedings of the 1st Asian Conference on Machine Learning (ACML), Lecture Notes in Computer Science, Springer, 2009. |
| [27] | Maurice Bruynooghe, Broes De Cat, Jochen Drijkoningen, Daan Fierens, Jan Goos, Bernd Gutmann, Angelika Kimmig, Wouter Labeeuw, Steven Langenaken, Niels Landwehr, Wannes Meert, Ewoud Nuyts, Robin Pellegrims, Roel Rymenants, Stefan Segers, Ingo Thon, Jelle Van Eyck, Guy Van den Broeck, Tine Vangansewinkel, Lucie Van Hove, Joost Vennekens, Timmy Weytjens and Luc De Raedt. An exercise with statistical relational learning systems, In International Workshop on Statistical Relational Learning (Pedro Domingos, Kristian Kersting, eds.), 2009. |
| [26] | Guy Van den Broeck. Algorithms and assessment in no-limit computer poker, Master's thesis, KU Leuven, 2009. Alcatel-Lucent Innovation Award |
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