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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
- 2023
[c112]Anji Liu, Hongming Xu, Guy Van den Broeck, Yitao Liang:
Out-of-Distribution Generalization by Neural-Symbolic Joint Training. AAAI 2023: 12252-12259- [c111]Nikil Roashan Selvam, Guy Van den Broeck, YooJung Choi:
Certifying Fairness of Probabilistic Circuits. AAAI 2023: 12278-12286 - [c110]Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck:
Semantic Strengthening of Neuro-Symbolic Learning. AISTATS 2023: 10252-10261 - [c109]Nikil Roashan Selvam, Honghua Zhang, Guy Van den Broeck:
Mixtures of All Trees. AISTATS 2023: 11043-11058 - [c108]Kareem Ahmed, Zhe Zeng, Mathias Niepert, Guy Van den Broeck:
SIMPLE: A Gradient Estimator for k-Subset Sampling. ICLR 2023 - [c107]Anji Liu, Honghua Zhang, Guy Van den Broeck:
Scaling Up Probabilistic Circuits by Latent Variable Distillation. ICLR 2023 - [c106]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 - [c105]Honghua Zhang, Meihua Dang, Nanyun Peng, Guy Van den Broeck:
Tractable Control for Autoregressive Language Generation. ICML 2023: 40932-40945 - [c104]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 - [c103]Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari:
Semantic Probabilistic Layers for Neuro-Symbolic Learning. NeSy 2023: 413 - [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 - [i75]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) - [i74]Nikil Roashan Selvam, Honghua Zhang, Guy Van den Broeck:
Mixtures of All Trees. CoRR abs/2302.14202 (2023) - [i73]Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck:
Semantic Strengthening of Neuro-Symbolic Learning. CoRR abs/2302.14207 (2023) - [i72]Honghua Zhang, Meihua Dang, Nanyun Peng, Guy Van den Broeck:
Tractable Control for Autoregressive Language Generation. CoRR abs/2304.07438 (2023) - [i71]Zhe Zeng, Guy Van den Broeck:
Collapsed Inference for Bayesian Deep Learning. CoRR abs/2306.09686 (2023) - [i70]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) - [i69]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) - [i68]Daniel Israel, Aditya Grover, Guy Van den Broeck:
High Dimensional Causal Inference with Variational Backdoor Adjustment. CoRR abs/2310.06100 (2023) - [i67]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) - 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 - [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 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,