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ORCIDGuy Van den Broeck
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- affiliation: University of California, Los Angeles, Computer Science Department
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2020 – today
- 2025
[c127]Benjie Wang, Guy Van den Broeck:
On the Relationship Between Monotone and Squared Probabilistic Circuits. AAAI 2025: 21026-21034- [c126]Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck:
Controllable Generation via Locally Constrained Resampling. ICLR 2025 - [c125]Anji Liu, Oliver Broadrick, Mathias Niepert, Guy Van den Broeck:
Discrete Copula Diffusion. ICLR 2025 - [c124]Vinh Tong, Dung-Trung Hoang, Anji Liu, Guy Van den Broeck, Mathias Niepert:
Learning to Discretize Denoising Diffusion ODEs. ICLR 2025 - [i101]Ruoyan Li, Dipti Ranjan Sahu, Guy Van den Broeck, Zhe Zeng:
Deep Generative Models with Hard Linear Equality Constraints. CoRR abs/2502.05416 (2025) - [i100]Daniel Israel, Aditya Grover, Guy Van den Broeck:
Enabling Autoregressive Models to Fill In Masked Tokens. CoRR abs/2502.06901 (2025) - [i99]Anji Liu, Xuejie Liu, Dayuan Zhao, Mathias Niepert, Yitao Liang, Guy Van den Broeck:
Tractable Transformers for Flexible Conditional Generation. CoRR abs/2502.07616 (2025) - [i98]Vinh Tong, Yun Ye, Trung-Dung Hoang, Anji Liu, Guy Van den Broeck, Mathias Niepert:
Symmetry-Preserving Diffusion Models via Target Symmetrization. CoRR abs/2502.09890 (2025) - [i97]Renato Lui Geh, Zilei Shao, Guy Van den Broeck:
Adversarial Tokenization. CoRR abs/2503.02174 (2025) - [i96]Gwen Yidou Weng, Benjie Wang, Guy Van den Broeck:
TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation. CoRR abs/2504.18535 (2025) - 2024
- [j16]Poorva Garg
, Steven Holtzen, Guy Van den Broeck, Todd D. Millstein:
Bit Blasting Probabilistic Programs. Proc. ACM Program. Lang. 8(PLDI): 865-888 (2024) - [c123]Renato Lui Geh, Honghua Zhang, Kareem Ahmed, Benjie Wang, Guy Van den Broeck:
Where is the signal in tokenization space? EMNLP 2024: 3966-3979 - [c122]Anji Liu, Mathias Niepert, Guy Van den Broeck:
Image Inpainting via Tractable Steering of Diffusion Models. ICLR 2024 - [c121]Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert, Christopher Morris:
Probabilistically Rewired Message-Passing Neural Networks. ICLR 2024 - [c120]Anji Liu, Kareem Ahmed, Guy Van den Broeck:
Scaling Tractable Probabilistic Circuits: A Systems Perspective. ICML 2024 - [c119]Benjie Wang, Denis Deratani Mauá, Guy Van den Broeck, YooJung Choi:
A Compositional Atlas for Algebraic Circuits. NeurIPS 2024 - [c118]Xuejie Liu, Anji Liu, Guy Van den Broeck, Yitao Liang:
A Tractable Inference Perspective of Offline RL. NeurIPS 2024 - [c117]Honghua Zhang, Po-Nien Kung, Masahiro Yoshida, Guy Van den Broeck, Nanyun Peng:
Adaptable Logical Control for Large Language Models. NeurIPS 2024 - [c116]Oliver Broadrick, Honghua Zhang, Guy Van den Broeck:
Polynomial Semantics of Tractable Probabilistic Circuits. UAI 2024: 418-429 - [i95]Anji Liu, Mathias Niepert, Guy Van den Broeck:
Image Inpainting via Tractable Steering of Diffusion Models. CoRR abs/2401.03349 (2024) - [i94]Oliver Broadrick, Honghua Zhang, Guy Van den Broeck:
Polynomial Semantics of Tractable Probabilistic Circuits. CoRR abs/2402.09085 (2024) - [i93]Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin:
On the Challenges and Opportunities in Generative AI. CoRR abs/2403.00025 (2024) - [i92]Siyan Zhao, Daniel Israel, Guy Van den Broeck, Aditya Grover:
Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models. CoRR abs/2404.09529 (2024) - [i91]Antoine Amarilli, Marcelo Arenas, YooJung Choi, Mikaël Monet, Guy Van den Broeck, Benjie Wang:
A Circus of Circuits: Connections Between Decision Diagrams, Circuits, and Automata. CoRR abs/2404.09674 (2024) - [i90]Kareem Ahmed, Stefano Teso, Paolo Morettin, Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Yitao Liang, Eric Wang, Kai-Wei Chang, Andrea Passerini, Guy Van den Broeck:
Semantic Loss Functions for Neuro-Symbolic Structured Prediction. CoRR abs/2405.07387 (2024) - [i89]Vinh Tong, Anji Liu, Trung-Dung Hoang, Guy Van den Broeck, Mathias Niepert:
Learning to Discretize Denoising Diffusion ODEs. CoRR abs/2405.15506 (2024) - [i88]Anji Liu, Kareem Ahmed, Guy Van den Broeck:
Scaling Tractable Probabilistic Circuits: A Systems Perspective. CoRR abs/2406.00766 (2024) - [i87]Honghua Zhang, Po-Nien Kung, Masahiro Yoshida, Guy Van den Broeck, Nanyun Peng:
Adaptable Logical Control for Large Language Models. CoRR abs/2406.13892 (2024) - [i86]Benjie Wang, Guy Van den Broeck:
On the Relationship Between Monotone and Squared Probabilistic Circuits. CoRR abs/2408.00876 (2024) - [i85]Oliver Broadrick, William X. Cao, Benjie Wang, Martin Trapp, Guy Van den Broeck:
Probabilistic Circuits for Cumulative Distribution Functions. CoRR abs/2408.04229 (2024) - [i84]Renato Lui Geh, Honghua Zhang, Kareem Ahmed, Benjie Wang, Guy Van den Broeck:
Where is the signal in tokenization space? CoRR abs/2408.08541 (2024) - [i83]Anji Liu, Oliver Broadrick, Mathias Niepert, Guy Van den Broeck:
Discrete Copula Diffusion. CoRR abs/2410.01949 (2024) - [i82]Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck:
Controllable Generation via Locally Constrained Resampling. CoRR abs/2410.13111 (2024) - [i81]Honghua Zhang, Benjie Wang, Marcelo Arenas, Guy Van den Broeck:
Restructuring Tractable Probabilistic Circuits. CoRR abs/2411.12256 (2024) - [i80]Benjie Wang, Denis Deratani Mauá, Guy Van den Broeck, YooJung Choi:
A Compositional Atlas for Algebraic Circuits. CoRR abs/2412.05481 (2024) - 2023
- [c115]Anji Liu, Hongming Xu, Guy Van den Broeck, Yitao Liang:
Out-of-Distribution Generalization by Neural-Symbolic Joint Training. AAAI 2023: 12252-12259 - [c114]Nikil Roashan Selvam, Guy Van den Broeck, YooJung Choi:
Certifying Fairness of Probabilistic Circuits. AAAI 2023: 12278-12286 - [c113]Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck:
Semantic Strengthening of Neuro-Symbolic Learning. AISTATS 2023: 10252-10261 - [c112]Nikil Roashan Selvam, Honghua Zhang, Guy Van den Broeck:
Mixtures of All Trees. AISTATS 2023: 11043-11058 - [c111]Kareem Ahmed, Zhe Zeng, Mathias Niepert, Guy Van den Broeck:
SIMPLE: A Gradient Estimator for k-Subset Sampling. ICLR 2023 - [c110]Anji Liu, Honghua Zhang, Guy Van den Broeck:
Scaling Up Probabilistic Circuits by Latent Variable Distillation. ICLR 2023 - [c109]Xuejie Liu, Anji Liu, Guy Van den Broeck, Yitao Liang:
Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits. ICML 2023: 21825-21838 - [c108]Honghua Zhang, Meihua Dang, Nanyun Peng, Guy Van den Broeck:
Tractable Control for Autoregressive Language Generation. ICML 2023: 40932-40945 - [c107]Honghua Zhang, Liunian Harold Li, Tao Meng, Kai-Wei Chang, Guy Van den Broeck:
On the Paradox of Learning to Reason from Data. IJCAI 2023: 3365-3373 - [c106]Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari:
Semantic Probabilistic Layers for Neuro-Symbolic Learning. NeSy 2023: 413 - [c105]Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck:
A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints. NeurIPS 2023 - [c104]Vinay Shukla, Zhe Zeng, Kareem Ahmed, Guy Van den Broeck:
A Unified Approach to Count-Based Weakly Supervised Learning. NeurIPS 2023 - [c103]Zhe Zeng, Guy Van den Broeck:
Collapsed Inference for Bayesian Deep Learning. NeurIPS 2023 - [c102]William X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen, Todd D. Millstein, Guy Van den Broeck:
Scaling integer arithmetic in probabilistic programs. UAI 2023: 260-270 - [p1]Kareem Ahmed, Stefano Teso, Paolo Morettin, Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Yitao Liang, Eric Wang, Kai-Wei Chang, Andrea Passerini, Guy Van den Broeck:
Semantic Loss Functions for Neuro-Symbolic Structured Prediction. Compendium of Neurosymbolic Artificial Intelligence 2023: 485-505 - [i79]Xuejie Liu, Anji Liu, Guy Van den Broeck, Yitao Liang:
Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits. CoRR abs/2302.08086 (2023) - [i78]Nikil Roashan Selvam, Honghua Zhang, Guy Van den Broeck:
Mixtures of All Trees. CoRR abs/2302.14202 (2023) - [i77]Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck:
Semantic Strengthening of Neuro-Symbolic Learning. CoRR abs/2302.14207 (2023) - [i76]Honghua Zhang, Meihua Dang, Nanyun Peng, Guy Van den Broeck:
Tractable Control for Autoregressive Language Generation. CoRR abs/2304.07438 (2023) - [i75]Zhe Zeng, Guy Van den Broeck:
Collapsed Inference for Bayesian Deep Learning. CoRR abs/2306.09686 (2023) - [i74]William X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen, Todd D. Millstein, Guy Van den Broeck:
Scaling Integer Arithmetic in Probabilistic Programs. CoRR abs/2307.13837 (2023) - [i73]Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert, Christopher Morris:
Probabilistically Rewired Message-Passing Neural Networks. CoRR abs/2310.02156 (2023) - [i72]Daniel Israel, Aditya Grover, Guy Van den Broeck:
High Dimensional Causal Inference with Variational Backdoor Adjustment. CoRR abs/2310.06100 (2023) - [i71]Xuejie Liu, Anji Liu, Guy Van den Broeck, Yitao Liang:
Expressive Modeling Is Insufficient for Offline RL: A Tractable Inference Perspective. CoRR abs/2311.00094 (2023) - [i70]Vinay Shukla, Zhe Zeng, Kareem Ahmed, Guy Van den Broeck:
A Unified Approach to Count-Based Weakly-Supervised Learning. CoRR abs/2311.13718 (2023) - [i69]Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck:
A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints. CoRR abs/2312.03905 (2023) - [i68]Poorva Garg, Steven Holtzen, Guy Van den Broeck, Todd D. Millstein:
Bit Blasting Probabilistic Programs. CoRR abs/2312.05706 (2023) - [i67]Sebastian Junges, Joost-Pieter Katoen, Scott Sanner, Guy Van den Broeck, Bahare Salmani:
Scalable Analysis of Probabilistic Models and Programs (Dagstuhl Seminar 23241). Dagstuhl Reports 13(6): 1-21 (2023) - 2022
- [j15]Meihua Dang, Antonio Vergari, Guy Van den Broeck:
Strudel: A fast and accurate learner of structured-decomposable probabilistic circuits. Int. J. Approx. Reason. 140: 92-115 (2022) - [j14]Guy Van den Broeck, Anton Lykov, Maximilian Schleich, Dan Suciu:
On the Tractability of SHAP Explanations. J. Artif. Intell. Res. 74: 851-886 (2022) - [c101]Kareem Ahmed, Tao Li, Thy Ton, Quan Guo, Kai-Wei Chang, Parisa Kordjamshidi, Vivek Srikumar, Guy Van den Broeck, Sameer Singh:
PYLON: A PyTorch Framework for Learning with Constraints. AAAI 2022: 13152-13154 - [c100]YooJung Choi, Tal Friedman, Guy Van den Broeck:
Solving Marginal MAP Exactly by Probabilistic Circuit Transformations. AISTATS 2022: 10196-10208 - [c99]Anji Liu, Stephan Mandt, Guy Van den Broeck:
Lossless Compression with Probabilistic Circuits. ICLR 2022 - [c98]Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari:
Semantic Probabilistic Layers for Neuro-Symbolic Learning. NeurIPS 2022 - [c97]Meihua Dang, Anji Liu, Guy Van den Broeck:
Sparse Probabilistic Circuits via Pruning and Growing. NeurIPS 2022 - [c96]Meihua Dang, Anji Liu, Xinzhu Wei, Sriram Sankararaman, Guy Van den Broeck:
Tractable and Expressive Generative Models of Genetic Variation Data. RECOMB 2022: 356-357 - [c95]Kareem Ahmed, Eric Wang, Kai-Wei Chang, Guy Van den Broeck:
Neuro-symbolic entropy regularization. UAI 2022: 43-53 - [i66]Kareem Ahmed, Eric Wang, Kai-Wei Chang, Guy Van den Broeck:
Neuro-Symbolic Entropy Regularization. CoRR abs/2201.11250 (2022) - [i65]Honghua Zhang, Liunian Harold Li, Tao Meng, Kai-Wei Chang, Guy Van den Broeck:
On the Paradox of Learning to Reason from Data. CoRR abs/2205.11502 (2022) - [i64]Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari:
Semantic Probabilistic Layers for Neuro-Symbolic Learning. CoRR abs/2206.00426 (2022) - [i63]Kareem Ahmed, Zhe Zeng, Mathias Niepert, Guy Van den Broeck:
SIMPLE: A Gradient Estimator for k-Subset Sampling. CoRR abs/2210.01941 (2022) - [i62]Anji Liu, Honghua Zhang, Guy Van den Broeck:
Scaling Up Probabilistic Circuits by Latent Variable Distillation. CoRR abs/2210.04398 (2022) - [i61]Meihua Dang, Anji Liu, Guy Van den Broeck:
Sparse Probabilistic Circuits via Pruning and Growing. CoRR abs/2211.12551 (2022) - [i60]Nikil Roashan Selvam, Guy Van den Broeck, YooJung Choi:
Certifying Fairness of Probabilistic Circuits. CoRR abs/2212.02474 (2022) - 2021
- [j13]Ismail Ilkan Ceylan, Adnan Darwiche, Guy Van den Broeck:
Open-world probabilistic databases: Semantics, algorithms, complexity. Artif. Intell. 295: 103474 (2021) - [c94]Guy Van den Broeck, Anton Lykov, Maximilian Schleich, Dan Suciu:
On the Tractability of SHAP Explanations. AAAI 2021: 6505-6513 - [c93]YooJung Choi, Meihua Dang, Guy Van den Broeck:
Group Fairness by Probabilistic Modeling with Latent Fair Decisions. AAAI 2021: 12051-12059 - [c92]Meihua Dang, Pasha Khosravi, Yitao Liang, Antonio Vergari, Guy Van den Broeck:
Juice: A Julia Package for Logic and Probabilistic Circuits. AAAI 2021: 16020-16023 - [c91]Yipeng Huang, Steven Holtzen, Todd D. Millstein, Guy Van den Broeck, Margaret Martonosi:
Logical abstractions for noisy variational Quantum algorithm simulation. ASPLOS 2021: 456-472 - [c90]Steven Holtzen, Sebastian Junges, Marcell Vazquez-Chanlatte, Todd D. Millstein, Sanjit A. Seshia, Guy Van den Broeck:
Model Checking Finite-Horizon Markov Chains with Probabilistic Inference. CAV (2) 2021: 577-601 - [c89]Guy Van den Broeck:
From Probabilistic Circuits to Probabilistic Programs and Back. ICAART (1) 2021: 9 - [c88]Honghua Zhang, Brendan Juba, Guy Van den Broeck:
Probabilistic Generating Circuits. ICML 2021: 12447-12457 - [c87]Eric Wang, Pasha Khosravi, Guy Van den Broeck:
Probabilistic Sufficient Explanations. IJCAI 2021: 3082-3088 - [c86]Kareem Ahmed, Tao Li, Thy Ton, Quan Guo, Kai-Wei Chang, Parisa Kordjamshidi, Vivek Srikumar, Guy Van den Broeck, Sameer Singh:
Pylon: A PyTorch Framework for Learning with Constraints. NeurIPS (Competition and Demos) 2021: 319-324 - [c85]Anji Liu, Guy Van den Broeck:
Tractable Regularization of Probabilistic Circuits. NeurIPS 2021: 3558-3570 - [c84]Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck:
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference. NeurIPS 2021: 13189-13201 - [c83]Wenzhe Li, Zhe Zeng, Antonio Vergari, Guy Van den Broeck:
Tractable computation of expected kernels. UAI 2021: 1163-1173 - [d1]Steven Holtzen, Sebastian Junges, Marcell Vazquez-Chanlatte, Todd D. Millstein, Sanjit A. Seshia, Guy Van den Broeck:
Experiments for 'Model Checking Finite-Horizon Markov Chains with Probabilistic Inference'. Zenodo, 2021
- [i59]Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck:
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries. CoRR abs/2102.06137 (2021) - [i58]Honghua Zhang, Brendan Juba, Guy Van den Broeck:
Probabilistic Generating Circuits. CoRR abs/2102.09768 (2021) - [i57]Wenzhe Li, Zhe Zeng, Antonio Vergari, Guy Van den Broeck:
Tractable Computation of Expected Kernels by Circuits. CoRR abs/2102.10562 (2021) - [i56]Kareem Ahmed, Eric Wang, Guy Van den Broeck, Kai-Wei Chang:
Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction. CoRR abs/2103.11062 (2021) - [i55]Yipeng Huang, Steven Holtzen, Todd D. Millstein, Guy Van den Broeck, Margaret Martonosi:
Logical Abstractions for Noisy Variational Quantum Algorithm Simulation. CoRR abs/2103.17226 (2021) - [i54]Eric Wang, Pasha Khosravi, Guy Van den Broeck:
Probabilistic Sufficient Explanations. CoRR abs/2105.10118 (2021) - [i53]Steven Holtzen, Sebastian Junges, Marcell Vazquez-Chanlatte, Todd D. Millstein, Sanjit A. Seshia, Guy Van den Broeck:
Model Checking Finite-Horizon Markov Chains with Probabilistic Inference. CoRR abs/2105.12326 (2021) - [i52]Anji Liu, Guy Van den Broeck:
Tractable Regularization of Probabilistic Circuits. CoRR abs/2106.02264 (2021) - [i51]Rushil Gupta, Vishal Sharma, Yash Jain, Yitao Liang, Guy Van den Broeck, Parag Singla:
Towards an Interpretable Latent Space in Structured Models for Video Prediction. CoRR abs/2107.07713 (2021) - [i50]Yu-Hsi Cheng, Todd D. Millstein, Guy Van den Broeck, Steven Holtzen:
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs. CoRR abs/2110.10284 (2021) - [i49]YooJung Choi, Tal Friedman, Guy Van den Broeck:
Solving Marginal MAP Exactly by Probabilistic Circuit Transformations. CoRR abs/2111.04833 (2021) - [i48]Anji Liu, Stephan Mandt, Guy Van den Broeck:
Lossless Compression with Probabilistic Circuits. CoRR abs/2111.11632 (2021) - 2020
- [j12]Krzysztof Gajowniczek, Yitao Liang, Tal Friedman, Tomasz S. Zabkowski, Guy Van den Broeck:
Semantic and Generalized Entropy Loss Functions for Semi-Supervised Deep Learning. Entropy 22(3): 334 (2020) - [j11]Steven Holtzen, Guy Van den Broeck, Todd D. Millstein:
Scaling exact inference for discrete probabilistic programs. Proc. ACM Program. Lang. 4(OOPSLA): 140:1-140:31 (2020) - [c82]YooJung Choi, Golnoosh Farnadi, Behrouz Babaki, Guy Van den Broeck:
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns. AAAI 2020: 10077-10084 - [c81]Anji Liu, Yitao Liang, Guy Van den Broeck:
Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration. AAMAS 2020: 753-761 - [c80]Albert Zhao, Tong He, Yitao Liang, Haibin Huang, Guy Van den Broeck, Stefano Soatto:
SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning. CoRL 2020: 156-175 - [c79]Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani:
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. ICML 2020: 7563-7574 - [c78]Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck:
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing. ICML 2020: 10990-11000 - [c77]Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Guy Van den Broeck, Marian Verhelst:
Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams. IDA 2020: 184-196 - [c76]Aishwarya Sivaraman, Golnoosh Farnadi, Todd D. Millstein, Guy Van den Broeck:
Counterexample-Guided Learning of Monotonic Neural Networks. NeurIPS 2020 - [c75]Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck:
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations. NeurIPS 2020 - [c74]Meihua Dang, Antonio Vergari, Guy Van den Broeck:
Strudel: Learning Structured-Decomposable Probabilistic Circuits. PGM 2020: 137-148 - [c73]Honghua Zhang, Steven Holtzen, Guy Van den Broeck:
On the Relationship Between Probabilistic Circuits and Determinantal Point Processes. UAI 2020: 1188-1197 - [c72]Tal Friedman, Guy Van den Broeck:
Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings. UAI 2020: 1268-1277 - [i47]Tal Friedman, Guy Van den Broeck:
Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings. CoRR abs/2002.10029 (2020) - [i46]Anji Liu, Yitao Liang, Guy Van den Broeck:
Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration. CoRR abs/2002.10738 (2020) - [i45]Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck:
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing. CoRR abs/2003.00126 (2020) - [i44]Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani:
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. CoRR abs/2004.06231 (2020) - [i43]Steven Holtzen, Guy Van den Broeck, Todd D. Millstein:
Dice: Compiling Discrete Probabilistic Programs for Scalable Inference. CoRR abs/2005.09089 (2020) - [i42]Anji Liu, Yitao Liang, Ji Liu, Guy Van den Broeck, Jianshu Chen:
On Effective Parallelization of Monte Carlo Tree Search. CoRR abs/2006.08785 (2020) - [i41]Aishwarya Sivaraman, Golnoosh Farnadi, Todd D. Millstein, Guy Van den Broeck:
Counterexample-Guided Learning of Monotonic Neural Networks. CoRR abs/2006.08852 (2020) - [i40]Honghua Zhang, Steven Holtzen, Guy Van den Broeck:
On the Relationship Between Probabilistic Circuits and Determinantal Point Processes. CoRR abs/2006.15233 (2020) - [i39]Pasha Khosravi, Antonio Vergari, YooJung Choi, Yitao Liang, Guy Van den Broeck:
Handling Missing Data in Decision Trees: A Probabilistic Approach. CoRR abs/2006.16341 (2020) - [i38]Meihua Dang, Antonio Vergari, Guy Van den Broeck:
Strudel: Learning Structured-Decomposable Probabilistic Circuits. CoRR abs/2007.09331 (2020) - [i37]Guy Van den Broeck, Anton Lykov, Maximilian Schleich, Dan Suciu:
On the Tractability of SHAP Explanations. CoRR abs/2009.08634 (2020) - [i36]YooJung Choi, Meihua Dang, Guy Van den Broeck:
Group Fairness by Probabilistic Modeling with Latent Fair Decisions. CoRR abs/2009.09031 (2020) - [i35]Kristian Kersting, Miryung Kim, Guy Van den Broeck, Thomas Zimmermann:
SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091). Dagstuhl Reports 10(2): 76-87 (2020)
2010 – 2019
- 2019
- [c71]Yitao Liang, Guy Van den Broeck:
Learning Logistic Circuits. AAAI 2019: 4277-4286 - [c70]Tal Friedman, Guy Van den Broeck:
On Constrained Open-World Probabilistic Databases. AKBC 2019 - [c69]Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van den Broeck, Luc De Raedt:
Scalable Rule Learning in Probabilistic Knowledge Bases. AKBC 2019 - [c68]Aishwarya Sivaraman, Tianyi Zhang, Guy Van den Broeck, Miryung Kim:
Active inductive logic programming for code search. ICSE 2019: 292-303 - [c67]Pasha Khosravi, Yitao Liang, YooJung Choi, Guy Van den Broeck:
What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features. IJCAI 2019: 2716-2724 - [c66]Tal Friedman, Guy Van den Broeck:
On Constrained Open-World Probabilistic Databases. IJCAI 2019: 5722-5729 - [c65]Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Guy Van den Broeck, Marian Verhelst:
On Hardware-Aware Probabilistic Frameworks for Resource Constrained Embedded Applications. EMC2@NeurIPS 2019: 66-70 - [c64]Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari, Guy Van den Broeck:
On Tractable Computation of Expected Predictions. NeurIPS 2019: 11167-11178 - [c63]Andy Shih, Guy Van den Broeck, Paul Beame, Antoine Amarilli:
Smoothing Structured Decomposable Circuits. NeurIPS 2019: 11412-11422 - [c62]Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst, Guy Van den Broeck:
Towards Hardware-Aware Tractable Learning of Probabilistic Models. NeurIPS 2019: 13726-13736 - [c61]Zhe Zeng, Guy Van den Broeck:
Efficient Search-Based Weighted Model Integration. UAI 2019: 175-185 - [c60]Steven Holtzen, Todd D. Millstein, Guy Van den Broeck:
Generating and Sampling Orbits for Lifted Probabilistic Inference. UAI 2019: 985-994 - [i34]Tal Friedman, Guy Van den Broeck:
On Constrained Open-World Probabilistic Databases. CoRR abs/1902.10677 (2019) - [i33]Yitao Liang, Guy Van den Broeck:
Learning Logistic Circuits. CoRR abs/1902.10798 (2019) - [i32]Pasha Khosravi, Yitao Liang, YooJung Choi, Guy Van den Broeck:
What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features. CoRR abs/1903.01620 (2019) - [i31]Steven Holtzen, Todd D. Millstein, Guy Van den Broeck:
Generating and Sampling Orbits for Lifted Probabilistic Inference. CoRR abs/1903.04672 (2019) - [i30]Zhe Zeng, Guy Van den Broeck:
Efficient Search-Based Weighted Model Integration. CoRR abs/1903.05334 (2019) - [i29]Steven Holtzen, Todd D. Millstein, Guy Van den Broeck:
Symbolic Exact Inference for Discrete Probabilistic Programs. CoRR abs/1904.02079 (2019) - [i28]Andy Shih, Guy Van den Broeck, Paul Beame, Antoine Amarilli:
Smoothing Structured Decomposable Circuits. CoRR abs/1906.00311 (2019) - [i27]YooJung Choi, Golnoosh Farnadi, Behrouz Babaki, Guy Van den Broeck:
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns. CoRR abs/1906.03843 (2019) - [i26]Zhe Zeng, Fanqi Yan, Paolo Morettin, Antonio Vergari, Guy Van den Broeck:
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing. CoRR abs/1909.09362 (2019) - [i25]Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari, Guy Van den Broeck:
On Tractable Computation of Expected Predictions. CoRR abs/1910.02182 (2019) - [i24]Albert Zhao, Tong He, Yitao Liang, Haibin Huang, Guy Van den Broeck, Stefano Soatto:
LaTeS: Latent Space Distillation for Teacher-Student Driving Policy Learning. CoRR abs/1912.02973 (2019) - 2018
- [c59]Steven Holtzen, Guy Van den Broeck, Todd D. Millstein:
Sound Abstraction and Decomposition of Probabilistic Programs. ICML 2018: 2004-2013 - [c58]Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, Guy Van den Broeck:
A Semantic Loss Function for Deep Learning with Symbolic Knowledge. ICML 2018: 5498-5507 - [c57]YooJung Choi, Guy Van den Broeck:
On Robust Trimming of Bayesian Network Classifiers. IJCAI 2018: 5002-5009 - [c56]Tal Friedman, Guy Van den Broeck:
Approximate Knowledge Compilation by Online Collapsed Importance Sampling. NeurIPS 2018: 8035-8045 - [i23]YooJung Choi, Guy Van den Broeck:
On Robust Trimming of Bayesian Network Classifiers. CoRR abs/1805.11243 (2018) - [i22]Tal Friedman, Guy Van den Broeck:
Approximate Knowledge Compilation by Online Collapsed Importance Sampling. CoRR abs/1805.12565 (2018) - [i21]Aishwarya Sivaraman, Tianyi Zhang, Guy Van den Broeck, Miryung Kim:
Active Inductive Logic Programming for Code Search. CoRR abs/1812.05265 (2018) - 2017
- [j10]Guy Van den Broeck, Dan Suciu:
Query Processing on Probabilistic Data: A Survey. Found. Trends Databases 7(3-4): 197-341 (2017) - [j9]Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
Algebraic model counting. J. Appl. Log. 22: 46-62 (2017) - [c55]Shahroze Kabir, Frederic Sala, Guy Van den Broeck, Lara Dolecek:
Coded machine learning: Joint informed replication and learning for linear regression. Allerton 2017: 1248-1255 - [c54]Anna L. D. Latour, Behrouz Babaki, Anton Dries, Angelika Kimmig, Guy Van den Broeck, Siegfried Nijssen:
Combining Stochastic Constraint Optimization and Probabilistic Programming - From Knowledge Compilation to Constraint Solving. CP 2017: 495-511 - [c53]YooJung Choi, Adnan Darwiche, Guy Van den Broeck:
Optimal Feature Selection for Decision Robustness in Bayesian Networks. IJCAI 2017: 1554-1560 - [c52]Ismail Ilkan Ceylan, Adnan Darwiche, Guy Van den Broeck:
Open-World Probabilistic Databases: An Abridged Report. IJCAI 2017: 4796-4800 - [c51]Steven Holtzen, Todd D. Millstein, Guy Van den Broeck:
Probabilistic Program Abstractions. UAI 2017 - [c50]Yitao Liang, Jessa Bekker, Guy Van den Broeck:
Learning the Structure of Probabilistic Sentential Decision Diagrams. UAI 2017 - [i20]Frederic Sala, Shahroze Kabir, Guy Van den Broeck, Lara Dolecek:
Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification. CoRR abs/1703.02641 (2017) - [i19]Steven Holtzen, Todd D. Millstein, Guy Van den Broeck:
Probabilistic Program Abstractions. CoRR abs/1705.09970 (2017) - [i18]Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, David Poole:
Domain Recursion for Lifted Inference with Existential Quantifiers. CoRR abs/1707.07763 (2017) - [i17]Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, Guy Van den Broeck:
A Semantic Loss Function for Deep Learning with Symbolic Knowledge. CoRR abs/1711.11157 (2017) - 2016
- [j8]Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck, Luc De Raedt:
Exploiting local and repeated structure in Dynamic Bayesian Networks. Artif. Intell. 232: 43-53 (2016) - [j7]Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt:
TP-Compilation for inference in probabilistic logic programs. Int. J. Approx. Reason. 78: 15-32 (2016) - [j6]Jan Van Haaren, Guy Van den Broeck, Wannes Meert, Jesse Davis:
Lifted generative learning of Markov logic networks. Mach. Learn. 103(1): 27-55 (2016) - [c49]Vaishak Belle, Guy Van den Broeck, Andrea Passerini:
Component Caching in Hybrid Domains with Piecewise Polynomial Densities. AAAI 2016: 3369-3375 - [c48]Kayvon Mazooji, Frederic Sala, Guy Van den Broeck, Lara Dolecek:
Robust channel coding strategies for machine learning data. Allerton 2016: 609-616 - [c47]Ismail Ilkan Ceylan, Adnan Darwiche, Guy Van den Broeck:
Open World Probabilistic Databases (Extended Abstract). Description Logics 2016 - [c46]Guy Van den Broeck:
First-Order Model Counting in a Nutshell. IJCAI 2016: 4086-4089 - [c45]Vaishak Belle, Guy Van den Broeck, Andrea Passerini:
Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report. IJCAI 2016: 4115-4119 - [c44]Ismail Ilkan Ceylan, Adnan Darwiche, Guy Van den Broeck:
Open-World Probabilistic Databases. KR 2016: 339-348 - [c43]Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, David Poole:
New Liftable Classes for First-Order Probabilistic Inference. NIPS 2016: 3117-3125 - [c42]Babak Salimi, Leopoldo E. Bertossi, Dan Suciu, Guy Van den Broeck:
Quantifying Causal Effects on Query Answering in Databases. TaPP 2016 - [i16]Babak Salimi, Leopoldo E. Bertossi, Dan Suciu, Guy Van den Broeck:
Quantifying Causal Effects on Query Answering in Databases. CoRR abs/1603.02705 (2016) - [i15]Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, David Poole:
New Liftable Classes for First-Order Probabilistic Inference. CoRR abs/1610.08445 (2016) - 2015
- [j5]Stefano V. Albrecht, André da Motta Salles Barreto, Darius Braziunas, David L. Buckeridge, Heriberto Cuayáhuitl, Nina Dethlefs, Markus Endres, Amir-massoud Farahmand, Mark Fox, Lutz Frommberger, Sam Ganzfried, Yolanda Gil, Sébastien Guillet, Lawrence E. Hunter, Arnav Jhala, Kristian Kersting, George Dimitri Konidaris, Freddy Lécué, Sheila A. McIlraith, Sriraam Natarajan, Zeinab Noorian, David Poole, Rémi Ronfard, Alessandro Saffiotti, Arash Shaban-Nejad, Biplav Srivastava, Gerald Tesauro, Rosario Uceda-Sosa, Guy Van den Broeck, Martijn van Otterlo, Byron C. Wallace, Paul Weng, Jenna Wiens, Jie Zhang:
Reports of the AAAI 2014 Conference Workshops. AI Mag. 36(1): 87-98 (2015) - [j4]Daan Fierens, Guy Van den Broeck, Joris Renkens, Dimitar Sht. Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens, Luc De Raedt:
Inference and learning in probabilistic logic programs using weighted Boolean formulas. Theory Pract. Log. Program. 15(3): 358-401 (2015) - [j3]Bart Bogaerts, Guy Van den Broeck:
Knowledge compilation of logic programs using approximation fixpoint theory. Theory Pract. Log. Program. 15(4-5): 464-480 (2015) - [c41]Guy Van den Broeck, Adnan Darwiche:
On the Role of Canonicity in Knowledge Compilation. AAAI 2015: 1641-1648 - [c40]Guy Van den Broeck, Mathias Niepert:
Lifted Probabilistic Inference for Asymmetric Graphical Models. AAAI 2015: 3599-3605 - [c39]Guy Van den Broeck:
Towards High-Level Probabilistic Reasoning with Lifted Inference. AAAI Spring Symposia 2015 - [c38]Arthur Choi, Guy Van den Broeck, Adnan Darwiche:
Probability Distributions over Structured Spaces. AAAI Spring Symposia 2015 - [c37]Guy Van den Broeck:
Symmetry in Probabilistic Databases. AMW 2015 - [c36]Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, Mathias Verbeke:
Inducing Probabilistic Relational Rules from Probabilistic Examples. IJCAI 2015: 1835-1843 - [c35]Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt:
Anytime Inference in Probabilistic Logic Programs with Tp-Compilation. IJCAI 2015: 1852-1858 - [c34]Vaishak Belle, Andrea Passerini, Guy Van den Broeck:
Probabilistic Inference in Hybrid Domains by Weighted Model Integration. IJCAI 2015: 2770-2776 - [c33]Arthur Choi, Guy Van den Broeck, Adnan Darwiche:
Tractable Learning for Structured Probability Spaces: A Case Study in Learning Preference Distributions. IJCAI 2015: 2861-2868 - [c32]Jessa Bekker, Jesse Davis, Arthur Choi, Adnan Darwiche, Guy Van den Broeck:
Tractable Learning for Complex Probability Queries. NIPS 2015: 2242-2250 - [c31]Anton Dries, Angelika Kimmig, Wannes Meert, Joris Renkens, Guy Van den Broeck, Jonas Vlasselaer, Luc De Raedt:
ProbLog2: Probabilistic Logic Programming. ECML/PKDD (3) 2015: 312-315 - [c30]Paul Beame, Guy Van den Broeck, Eric Gribkoff, Dan Suciu:
Symmetric Weighted First-Order Model Counting. PODS 2015: 313-328 - [c29]Vaishak Belle, Guy Van den Broeck, Andrea Passerini:
Hashing-Based Approximate Probabilistic Inference in Hybrid Domains. UAI 2015: 141-150 - [c28]Guy Van den Broeck, Karthika Mohan, Arthur Choi, Adnan Darwiche, Judea Pearl:
Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data. UAI 2015: 161-170 - [i14]Bart Bogaerts, Guy Van den Broeck:
Knowledge Compilation of Logic Programs Using Approximation Fixpoint Theory. CoRR abs/1507.06554 (2015) - 2014
- [j2]Eric Gribkoff, Dan Suciu, Guy Van den Broeck:
Lifted Probabilistic Inference: A Guide for the Database Researcher. IEEE Data Eng. Bull. 37(3): 6-17 (2014) - [c27]Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan, David Poole:
Preface. StarAI@AAAI 2014 - [c26]Eric Gribkoff, Guy Van den Broeck, Dan Suciu:
Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting. StarAI@AAAI 2014 - [c25]Mathias Niepert, Guy Van den Broeck:
Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference. AAAI 2014: 2467-2475 - [c24]Joris Renkens, Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics. AAAI 2014: 2490-2496 - [c23]Joris Renkens, Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics. StarAI@AAAI 2014 - [c22]Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck, Luc De Raedt:
Efficient Probabilistic Inference for Dynamic Relational Models. StarAI@AAAI 2014 - [c21]Guy Van den Broeck, Wannes Meert, Adnan Darwiche:
Skolemization for Weighted First-Order Model Counting. KR 2014 - [c20]Doga Kisa, Guy Van den Broeck, Arthur Choi, Adnan Darwiche:
Probabilistic Sentential Decision Diagrams. KR 2014 - [c19]Eric Gribkoff, Guy Van den Broeck, Dan Suciu:
Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting. UAI 2014: 280-289 - [i13]Mathias Niepert, Guy Van den Broeck:
Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference. CoRR abs/1401.1247 (2014) - [i12]Guy Van den Broeck, Adnan Darwiche:
On the Role of Canonicity in Bottom-up Knowledge Compilation. CoRR abs/1404.4089 (2014) - [i11]Eric Gribkoff, Guy Van den Broeck, Dan Suciu:
Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting. CoRR abs/1405.3250 (2014) - [i10]Guy Van den Broeck, Karthika Mohan, Arthur Choi, Judea Pearl:
Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data. CoRR abs/1411.7014 (2014) - [i9]Guy Van den Broeck, Mathias Niepert:
Lifted Probabilistic Inference for Asymmetric Graphical Models. CoRR abs/1412.0315 (2014) - [i8]Paul Beame, Guy Van den Broeck, Eric Gribkoff, Dan Suciu:
Symmetric Weighted First-Order Model Counting. CoRR abs/1412.1505 (2014) - 2013
- [b1]Guy Van den Broeck:
Lifted Inference and Learning in Statistical Relational Models (Eerste-orde inferentie en leren in statistische relationele modellen). Katholieke Universiteit Leuven, Belgium, 2013 - [c18]Guy Van den Broeck:
On the Complexity and Approximation of Binary Evidence in Lifted Inference. StarAI@AAAI 2013 - [c17]Guy Van den Broeck, Wannes Meert, Jesse Davis:
Lifted Generative Parameter Learning. StarAI@AAAI 2013 - [c16]Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel:
On the Completeness of Lifted Variable Elimination. StarAI@AAAI 2013 - [c15]Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel:
Completeness Results for Lifted Variable Elimination. AISTATS 2013: 572-580 - [c14]Guy Van den Broeck, Adnan Darwiche:
On the Complexity and Approximation of Binary Evidence in Lifted Inference. NIPS 2013: 2868-2876 - [i7]Daan Fierens, Guy Van den Broeck, Joris Renkens, Dimitar Sht. Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens, Luc De Raedt:
Inference and learning in probabilistic logic programs using weighted Boolean formulas. CoRR abs/1304.6810 (2013) - [i6]Guy Van den Broeck, Adnan Darwiche:
On the Complexity and Approximation of Binary Evidence in Lifted Inference. CoRR abs/1311.6591 (2013) - [i5]Guy Van den Broeck, Wannes Meert, Adnan Darwiche:
Skolemization for Weighted First-Order Model Counting. CoRR abs/1312.5378 (2013) - 2012
- [j1]Joris Renkens, Guy Van den Broeck, Siegfried Nijssen:
k-Optimal: a novel approximate inference algorithm for ProbLog. Mach. Learn. 89(3): 215-231 (2012) - [c13]Guy Van den Broeck, Jesse Davis:
Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference. AAAI 2012: 1961-1967 - [c12]Manfred Jaeger, Guy Van den Broeck:
Liftability of Probabilistic Inference: Upper and Lower Bounds. StarAI@UAI 2012 - [c11]Davide Nitti, Tinne De Laet, McElory Hoffmann, Ingo Thon, Guy Van den Broeck, Luc De Raedt:
A Particle Filter for Probabilistic Dynamic Relational Domains. StarAI@UAI 2012 - [c10]Guy Van den Broeck, Arthur Choi, Adnan Darwiche:
Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference. UAI 2012: 131-141 - [i4]Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann, Luc De Raedt:
Inference in Probabilistic Logic Programs using Weighted CNF's. CoRR abs/1202.3719 (2012) - [i3]Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel:
Lifted Variable Elimination: A Novel Operator and Completeness Results. CoRR abs/1208.3809 (2012) - [i2]Guy Van den Broeck, Arthur Choi, Adnan Darwiche:
Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference. CoRR abs/1210.4840 (2012) - [i1]Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
Algebraic Model Counting. CoRR abs/1211.4475 (2012) - 2011
- [c9]Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
An Algebraic Prolog for Reasoning about Possible Worlds. AAAI 2011: 209-214 - [c8]Guy Van den Broeck, Nima Taghipour, Wannes Meert, Jesse Davis, Luc De Raedt:
Lifted Probabilistic Inference by First-Order Knowledge Compilation. IJCAI 2011: 2178-2185 - [c7]Joris Renkens, Guy Van den Broeck, Siegfried Nijssen:
k-Optimal: A Novel Approximate Inference Algorithm for ProbLog. ILP 2011: 33-38 - [c6]Jan Van Haaren, Guy Van den Broeck:
Relational Learning for Football-Related Predictions. ILP (Late Breaking Papers) 2011: 237-244 - [c5]Guy Van den Broeck:
On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference. NIPS 2011: 1386-1394 - [c4]Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann, Luc De Raedt:
Inference in Probabilistic Logic Programs using Weighted CNF's. UAI 2011: 211-220 - 2010
- [c3]Guy Van den Broeck, Ingo Thon, Martijn van Otterlo, Luc De Raedt:
DTProbLog: A Decision-Theoretic Probabilistic Prolog. AAAI 2010: 1217-1222 - [c2]Ingo Thon, Bernd Gutmann, Guy Van den Broeck:
Probabilistic Programming for Planning Problems. StarAI@AAAI 2010
2000 – 2009
- 2009
- [c1]Guy Van den Broeck, Kurt Driessens, Jan Ramon:
Monte-Carlo Tree Search in Poker Using Expected Reward Distributions. ACML 2009: 367-381
Coauthor Index
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