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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
- 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)- [c118]Anji Liu, Mathias Niepert, Guy Van den Broeck:
Image Inpainting via Tractable Steering of Diffusion Models. ICLR 2024 - [c117]Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert, Christopher Morris:
Probabilistically Rewired Message-Passing Neural Networks. ICLR 2024 - [c116]Anji Liu, Kareem Ahmed, Guy Van den Broeck:
Scaling Tractable Probabilistic Circuits: A Systems Perspective. ICML 2024 - [i91]Anji Liu, Mathias Niepert, Guy Van den Broeck:
Image Inpainting via Tractable Steering of Diffusion Models. CoRR abs/2401.03349 (2024) - [i90]Oliver Broadrick, Honghua Zhang, Guy Van den Broeck:
Polynomial Semantics of Tractable Probabilistic Circuits. CoRR abs/2402.09085 (2024) - [i89]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) - [i88]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) - [i87]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) - [i86]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) - [i85]Vinh Tong, Anji Liu, Trung-Dung Hoang, Guy Van den Broeck, Mathias Niepert:
Learning to Discretize Denoising Diffusion ODEs. CoRR abs/2405.15506 (2024) - [i84]Anji Liu, Kareem Ahmed, Guy Van den Broeck:
Scaling Tractable Probabilistic Circuits: A Systems Perspective. CoRR abs/2406.00766 (2024) - [i83]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) - [i82]Benjie Wang, Guy Van den Broeck:
On the Relationship Between Monotone and Squared Probabilistic Circuits. CoRR abs/2408.00876 (2024) - [i81]Oliver Broadrick, William X. Cao, Benjie Wang, Martin Trapp, Guy Van den Broeck:
Probabilistic Circuits for Cumulative Distribution Functions. CoRR abs/2408.04229 (2024) - [i80]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) - 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.