Monte-Carlo Tree Search in Poker Using Expected Reward Distributions. - Guy Van den Broeck; Kurt Driessens; Jan Ramon (ACML, 2009)

DTProbLog: A Decision-Theoretic Probabilistic Prolog. - Guy Van den Broeck; Ingo Thon; Martijn van Otterlo; Luc De Raedt (AAAI, 2010)

Probabilistic Programming for Planning Problems. - Ingo Thon; Bernd Gutmann; Guy Van den Broeck (StarAI@AAAI, 2010)

An Algebraic Prolog for Reasoning about Possible Worlds. - Angelika Kimmig; Guy Van den Broeck; Luc De Raedt (AAAI, 2011)

Lifted Probabilistic Inference by First-Order Knowledge Compilation. - Guy Van den Broeck; Nima Taghipour; Wannes Meert; Jesse Davis; Luc De Raedt (IJCAI, 2011)

Relational Learning for Football-Related Predictions. - Jan Van Haaren; Guy Van den Broeck (ILP (Late Breaking Papers), 2011)

On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference. - Guy Van den Broeck (NIPS, 2011)

Inference in Probabilistic Logic Programs using Weighted CNF's. - Daan Fierens; Guy Van den Broeck; Ingo Thon; Bernd Gutmann; Luc De Raedt (UAI, 2011)

Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference. - Guy Van den Broeck; Jesse Davis (AAAI, 2012)

Liftability of Probabilistic Inference: Upper and Lower Bounds. - Manfred Jaeger; Guy Van den Broeck (StarAI@UAI, 2012)

A Particle Filter for Probabilistic Dynamic Relational Domains. - Davide Nitti 001; Tinne De Laet; McElory Hoffmann; Ingo Thon; Guy Van den Broeck; Luc De Raedt (StarAI@UAI, 2012)

Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference. - Guy Van den Broeck; Arthur Choi; Adnan Darwiche (UAI, 2012)

Lifted Variable Elimination: A Novel Operator and Completeness Results - Nima Taghipour; Daan Fierens; Guy Van den Broeck; Jesse Davis; Hendrik Blockeel (CoRR, 2012)

k-Optimal: a novel approximate inference algorithm for ProbLog. - Joris Renkens; Guy Van den Broeck; Siegfried Nijssen (Mach. Learn., 2012)

On the Complexity and Approximation of Binary Evidence in Lifted Inference. - Guy Van den Broeck (StarAI@AAAI, 2013)

Lifted Generative Parameter Learning. - Guy Van den Broeck; Wannes Meert; Jesse Davis (StarAI@AAAI, 2013)

On the Completeness of Lifted Variable Elimination. - Nima Taghipour; Daan Fierens; Guy Van den Broeck; Jesse Davis; Hendrik Blockeel (StarAI@AAAI, 2013)

Completeness Results for Lifted Variable Elimination. - Nima Taghipour; Daan Fierens; Guy Van den Broeck; Jesse Davis; Hendrik Blockeel (AISTATS, 2013)

On the Complexity and Approximation of Binary Evidence in Lifted Inference. - Guy Van den Broeck; Adnan Darwiche (NIPS, 2013)

Lifted Inference and Learning in Statistical Relational Models (Eerste-orde inferentie en leren in statistische relationele modellen). - Guy Van den Broeck (2013)

Preface. - Guy Van den Broeck; Kristian Kersting; Sriraam Natarajan; David Poole 001 (StarAI@AAAI, 2014)

Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting. - Eric Gribkoff; Guy Van den Broeck; Dan Suciu (StarAI@AAAI, 2014)

Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference. - Mathias Niepert; Guy Van den Broeck (AAAI, 2014)

Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics. - Joris Renkens; Angelika Kimmig; Guy Van den Broeck; Luc De Raedt (AAAI, 2014)

Efficient Probabilistic Inference for Dynamic Relational Models. - Jonas Vlasselaer; Wannes Meert; Guy Van den Broeck; Luc De Raedt (StarAI@AAAI, 2014)

Skolemization for Weighted First-Order Model Counting. - Guy Van den Broeck; Wannes Meert; Adnan Darwiche (KR, 2014)

Probabilistic Sentential Decision Diagrams. - Doga Kisa; Guy Van den Broeck; Arthur Choi; Adnan Darwiche (KR, 2014)

On the Role of Canonicity in Bottom-up Knowledge Compilation. - Guy Van den Broeck; Adnan Darwiche (CoRR, 2014)

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data. - Guy Van den Broeck; Karthika Mohan; Arthur Choi; Judea Pearl (CoRR, 2014)

Lifted Probabilistic Inference: A Guide for the Database Researcher. - Eric Gribkoff; Dan Suciu; Guy Van den Broeck (IEEE Data Eng. Bull., 2014)

On the Role of Canonicity in Knowledge Compilation. - Guy Van den Broeck; Adnan Darwiche (AAAI, 2015)

Lifted Probabilistic Inference for Asymmetric Graphical Models. - Guy Van den Broeck; Mathias Niepert (AAAI, 2015)

Towards High-Level Probabilistic Reasoning with Lifted Inference. - Guy Van den Broeck (AAAI Spring Symposia, 2015)

Probability Distributions over Structured Spaces. - Arthur Choi; Guy Van den Broeck; Adnan Darwiche (AAAI Spring Symposia, 2015)

Symmetry in Probabilistic Databases. - Guy Van den Broeck (AMW, 2015)

Probabilistic Inference in Hybrid Domains by Weighted Model Integration. - Vaishak Belle; Andrea Passerini; Guy Van den Broeck (IJCAI, 2015)

Tractable Learning for Structured Probability Spaces: A Case Study in Learning Preference Distributions. - Arthur Choi; Guy Van den Broeck; Adnan Darwiche (IJCAI, 2015)

Inducing Probabilistic Relational Rules from Probabilistic Examples. - Luc De Raedt; Anton Dries; Ingo Thon; Guy Van den Broeck; Mathias Verbeke (IJCAI, 2015)

Anytime Inference in Probabilistic Logic Programs with Tp-Compilation. - Jonas Vlasselaer; Guy Van den Broeck; Angelika Kimmig; Wannes Meert; Luc De Raedt (IJCAI, 2015)

Tractable Learning for Complex Probability Queries. - Jessa Bekker; Jesse Davis; Arthur Choi; Adnan Darwiche; Guy Van den Broeck (NIPS, 2015)

ProbLog2: Probabilistic Logic Programming. - Anton Dries; Angelika Kimmig; Wannes Meert; Joris Renkens; Guy Van den Broeck; Jonas Vlasselaer; Luc De Raedt (ECML/PKDD (3), 2015)

Symmetric Weighted First-Order Model Counting. - Paul Beame; Guy Van den Broeck; Eric Gribkoff; Dan Suciu (PODS, 2015)

Hashing-Based Approximate Probabilistic Inference in Hybrid Domains. - Vaishak Belle; Guy Van den Broeck; Andrea Passerini (UAI, 2015)

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data. - Guy Van den Broeck; Karthika Mohan; Arthur Choi; Adnan Darwiche; Judea Pearl (UAI, 2015)

Reports of the AAAI 2014 Conference Workshops. - 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; et al. (AI Mag., 2015)

Knowledge compilation of logic programs using approximation fixpoint theory. - Bart Bogaerts 001; Guy Van den Broeck (Theory Pract. Log. Program., 2015)

Inference and learning in probabilistic logic programs using weighted Boolean formulas. - Daan Fierens; Guy Van den Broeck; Joris Renkens; Dimitar Sht. Shterionov; Bernd Gutmann; Ingo Thon; Gerda Janssens; Luc De Raedt (Theory Pract. Log. Program., 2015)

Component Caching in Hybrid Domains with Piecewise Polynomial Densities. - Vaishak Belle; Guy Van den Broeck; Andrea Passerini (AAAI, 2016)

Robust channel coding strategies for machine learning data. - Kayvon Mazooji; Frederic Sala; Guy Van den Broeck; Lara Dolecek (Allerton, 2016)

Open World Probabilistic Databases (Extended Abstract). - Ismail Ilkan Ceylan; Adnan Darwiche; Guy Van den Broeck (Description Logics, 2016)

Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report. - Vaishak Belle; Guy Van den Broeck; Andrea Passerini (IJCAI, 2016)

First-Order Model Counting in a Nutshell. - Guy Van den Broeck (IJCAI, 2016)

Open-World Probabilistic Databases. - Ismail Ilkan Ceylan; Adnan Darwiche; Guy Van den Broeck (KR, 2016)

New Liftable Classes for First-Order Probabilistic Inference. - Seyed Mehran Kazemi; Angelika Kimmig; Guy Van den Broeck; David Poole 001 (NIPS, 2016)

Quantifying Causal Effects on Query Answering in Databases. - Babak Salimi; Leopoldo E. Bertossi; Dan Suciu; Guy Van den Broeck (TaPP, 2016)

Exploiting local and repeated structure in Dynamic Bayesian Networks. - Jonas Vlasselaer; Wannes Meert; Guy Van den Broeck; Luc De Raedt (Artif. Intell., 2016)

TP-Compilation for inference in probabilistic logic programs. - Jonas Vlasselaer; Guy Van den Broeck; Angelika Kimmig; Wannes Meert; Luc De Raedt (Int. J. Approx. Reason., 2016)

Lifted generative learning of Markov logic networks. - Jan Van Haaren; Guy Van den Broeck; Wannes Meert; Jesse Davis (Mach. Learn., 2016)

Coded machine learning: Joint informed replication and learning for linear regression. - Shahroze Kabir; Frederic Sala; Guy Van den Broeck; Lara Dolecek (Allerton, 2017)

Combining Stochastic Constraint Optimization and Probabilistic Programming - From Knowledge Compilation to Constraint Solving. - Anna L. D. Latour; Behrouz Babaki; Anton Dries; Angelika Kimmig; Guy Van den Broeck; Siegfried Nijssen (CP, 2017)

Open-World Probabilistic Databases: An Abridged Report. - Ismail Ilkan Ceylan; Adnan Darwiche; Guy Van den Broeck (IJCAI, 2017)

Optimal Feature Selection for Decision Robustness in Bayesian Networks. - YooJung Choi 001; Adnan Darwiche; Guy Van den Broeck (IJCAI, 2017)

Probabilistic Program Abstractions. - Steven Holtzen; Todd D. Millstein; Guy Van den Broeck (UAI, 2017)

Learning the Structure of Probabilistic Sentential Decision Diagrams. - Yitao Liang; Jessa Bekker; Guy Van den Broeck (UAI, 2017)

Domain Recursion for Lifted Inference with Existential Quantifiers. - Seyed Mehran Kazemi; Angelika Kimmig; Guy Van den Broeck; David Poole 001 (CoRR, 2017)

Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification. - Frederic Sala; Shahroze Kabir; Guy Van den Broeck; Lara Dolecek (CoRR, 2017)

Query Processing on Probabilistic Data: A Survey. - Guy Van den Broeck; Dan Suciu (Found. Trends Databases, 2017)

Algebraic model counting. - Angelika Kimmig; Guy Van den Broeck; Luc De Raedt (J. Appl. Log., 2017)

Sound Abstraction and Decomposition of Probabilistic Programs. - Steven Holtzen; Guy Van den Broeck; Todd D. Millstein (ICML, 2018)

A Semantic Loss Function for Deep Learning with Symbolic Knowledge. - Jingyi Xu; Zilu Zhang; Tal Friedman; Yitao Liang; Guy Van den Broeck (ICML, 2018)

On Robust Trimming of Bayesian Network Classifiers. - YooJung Choi 001; Guy Van den Broeck (IJCAI, 2018)

Approximate Knowledge Compilation by Online Collapsed Importance Sampling. - Tal Friedman; Guy Van den Broeck (NeurIPS, 2018)

Learning Logistic Circuits. - Yitao Liang; Guy Van den Broeck (AAAI, 2019)

On Constrained Open-World Probabilistic Databases. - Tal Friedman; Guy Van den Broeck (AKBC, 2019)

Scalable Rule Learning in Probabilistic Knowledge Bases. - Arcchit Jain; Tal Friedman; Ondrej Kuzelka; Guy Van den Broeck; Luc De Raedt (AKBC, 2019)

Active inductive logic programming for code search. - Aishwarya Sivaraman; Tianyi Zhang 001; Guy Van den Broeck; Miryung Kim (ICSE, 2019)

What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features. - Pasha Khosravi; Yitao Liang; YooJung Choi 001; Guy Van den Broeck (IJCAI, 2019)

On Tractable Computation of Expected Predictions. - Pasha Khosravi; YooJung Choi 001; Yitao Liang; Antonio Vergari; Guy Van den Broeck (NeurIPS, 2019)

On Hardware-Aware Probabilistic Frameworks for Resource Constrained Embedded Applications. - Laura Isabel Galindez Olascoaga; Wannes Meert; Nimish Shah; Guy Van den Broeck; Marian Verhelst (EMC2@NeurIPS, 2019)

Towards Hardware-Aware Tractable Learning of Probabilistic Models. - Laura Isabel Galindez Olascoaga; Wannes Meert; Nimish Shah; Marian Verhelst; Guy Van den Broeck (NeurIPS, 2019)

Smoothing Structured Decomposable Circuits. - Andy Shih; Guy Van den Broeck; Paul Beame; Antoine Amarilli (NeurIPS, 2019)

Generating and Sampling Orbits for Lifted Probabilistic Inference. - Steven Holtzen; Todd D. Millstein; Guy Van den Broeck (UAI, 2019)

Efficient Search-Based Weighted Model Integration. - Zhe Zeng; Guy Van den Broeck (UAI, 2019)

Symbolic Exact Inference for Discrete Probabilistic Programs. - Steven Holtzen; Todd D. Millstein; Guy Van den Broeck (CoRR, 2019)

Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing. - Zhe Zeng; Fanqi Yan; Paolo Morettin; Antonio Vergari; Guy Van den Broeck (CoRR, 2019)

LaTeS: Latent Space Distillation for Teacher-Student Driving Policy Learning. - Albert Zhao; Tong He 002; Yitao Liang; Haibin Huang; Guy Van den Broeck; Stefano Soatto (CoRR, 2019)

Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns. - YooJung Choi 001; Golnoosh Farnadi; Behrouz Babaki; Guy Van den Broeck (AAAI, 2020)

Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration. - Anji Liu; Yitao Liang; Guy Van den Broeck (AAMAS, 2020)

SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning. - Albert Zhao; Tong He 002; Yitao Liang; Haibin Huang; Guy Van den Broeck; Stefano Soatto (CoRL, 2020)

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. - Robert Peharz; Steven Lang; Antonio Vergari; Karl Stelzner; Alejandro Molina 001; Martin Trapp 001; Guy Van den Broeck; Kristian Kersting; Zoubin Ghahramani (ICML, 2020)

Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing. - Zhe Zeng; Paolo Morettin; Fanqi Yan; Antonio Vergari; Guy Van den Broeck (ICML, 2020)

Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams. - Laura Isabel Galindez Olascoaga; Wannes Meert; Nimish Shah; Guy Van den Broeck; Marian Verhelst (IDA, 2020)

Counterexample-Guided Learning of Monotonic Neural Networks. - Aishwarya Sivaraman; Golnoosh Farnadi; Todd D. Millstein; Guy Van den Broeck (NeurIPS, 2020)

Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations. - Zhe Zeng; Paolo Morettin; Fanqi Yan; Antonio Vergari; Guy Van den Broeck (NeurIPS, 2020)

Strudel: Learning Structured-Decomposable Probabilistic Circuits. - Meihua Dang; Antonio Vergari; Guy Van den Broeck (PGM, 2020)

Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings. - Tal Friedman; Guy Van den Broeck (UAI, 2020)

On the Relationship Between Probabilistic Circuits and Determinantal Point Processes. - Honghua Zhang; Steven Holtzen; Guy Van den Broeck (UAI, 2020)

Dice: Compiling Discrete Probabilistic Programs for Scalable Inference. - Steven Holtzen; Guy Van den Broeck; Todd D. Millstein (CoRR, 2020)

On Effective Parallelization of Monte Carlo Tree Search. - Anji Liu; Yitao Liang; Ji Liu; Guy Van den Broeck; Jianshu Chen (CoRR, 2020)

Handling Missing Data in Decision Trees: A Probabilistic Approach. - Pasha Khosravi; Antonio Vergari; YooJung Choi 001; Yitao Liang; Guy Van den Broeck (CoRR, 2020)

SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091). - Kristian Kersting; Miryung Kim; Guy Van den Broeck; Thomas Zimmermann 001 (Dagstuhl Reports, 2020)

Semantic and Generalized Entropy Loss Functions for Semi-Supervised Deep Learning. - Krzysztof Gajowniczek; Yitao Liang; Tal Friedman; Tomasz S. Zabkowski; Guy Van den Broeck (Entropy, 2020)

Scaling exact inference for discrete probabilistic programs. - Steven Holtzen; Guy Van den Broeck; Todd D. Millstein (Proc. ACM Program. Lang., 2020)

Group Fairness by Probabilistic Modeling with Latent Fair Decisions. - YooJung Choi 001; Meihua Dang; Guy Van den Broeck (AAAI, 2021)

Juice: A Julia Package for Logic and Probabilistic Circuits. - Meihua Dang; Pasha Khosravi; Yitao Liang; Antonio Vergari; Guy Van den Broeck (AAAI, 2021)

Logical abstractions for noisy variational Quantum algorithm simulation. - Yipeng Huang 001; Steven Holtzen; Todd D. Millstein; Guy Van den Broeck; Margaret Martonosi (ASPLOS, 2021)

Model Checking Finite-Horizon Markov Chains with Probabilistic Inference. - Steven Holtzen; Sebastian Junges; Marcell Vazquez-Chanlatte; Todd D. Millstein; Sanjit A. Seshia; Guy Van den Broeck (CAV (2), 2021)

From Probabilistic Circuits to Probabilistic Programs and Back. - Guy Van den Broeck (ICAART (1), 2021)

Probabilistic Generating Circuits. - Honghua Zhang; Brendan Juba; Guy Van den Broeck (ICML, 2021)

Probabilistic Sufficient Explanations. - Eric Wang; Pasha Khosravi; Guy Van den Broeck (IJCAI, 2021)

Tractable Regularization of Probabilistic Circuits. - Anji Liu; Guy Van den Broeck (NeurIPS, 2021)

A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference. - Antonio Vergari; YooJung Choi 001; Anji Liu; Stefano Teso; Guy Van den Broeck (NeurIPS, 2021)

Tractable computation of expected kernels. - Wenzhe Li; Zhe Zeng; Antonio Vergari; Guy Van den Broeck (UAI, 2021)

Open-world probabilistic databases: Semantics, algorithms, complexity. - Ismail Ilkan Ceylan; Adnan Darwiche; Guy Van den Broeck (Artif. Intell., 2021)

A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries. - Antonio Vergari; YooJung Choi 001; Anji Liu; Stefano Teso; Guy Van den Broeck (CoRR, 2021)

Tractable Computation of Expected Kernels by Circuits. - Wenzhe Li; Zhe Zeng; Antonio Vergari; Guy Van den Broeck (CoRR, 2021)

Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction. - Kareem Ahmed; Eric Wang; Guy Van den Broeck; Kai-Wei Chang (CoRR, 2021)

Towards an Interpretable Latent Space in Structured Models for Video Prediction. - Rushil Gupta; Vishal Sharma; Yash Jain; Yitao Liang; Guy Van den Broeck; Parag Singla (CoRR, 2021)

flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs. - Yu-Hsi Cheng; Todd D. Millstein; Guy Van den Broeck; Steven Holtzen (CoRR, 2021)

Experiments for 'Model Checking Finite-Horizon Markov Chains with Probabilistic Inference'. - Steven Holtzen; Sebastian Junges; Marcell Vazquez-Chanlatte; Todd D. Millstein; Sanjit A. Seshia; Guy Van den Broeck (Zenodo, 2021)

PYLON: A PyTorch Framework for Learning with Constraints. - Kareem Ahmed; Tao Li 039; Thy Ton; Quan Guo; Kai-Wei Chang; Parisa Kordjamshidi; Vivek Srikumar; Guy Van den Broeck; Sameer Singh 001 (AAAI, 2022)

Solving Marginal MAP Exactly by Probabilistic Circuit Transformations. - YooJung Choi 001; Tal Friedman; Guy Van den Broeck (AISTATS, 2022)

Lossless Compression with Probabilistic Circuits. - Anji Liu; Stephan Mandt; Guy Van den Broeck (ICLR, 2022)

Semantic Probabilistic Layers for Neuro-Symbolic Learning. - Kareem Ahmed; Stefano Teso; Kai-Wei Chang; Guy Van den Broeck; Antonio Vergari (NeurIPS, 2022)

Sparse Probabilistic Circuits via Pruning and Growing. - Meihua Dang; Anji Liu; Guy Van den Broeck (NeurIPS, 2022)

Tractable and Expressive Generative Models of Genetic Variation Data. - Meihua Dang; Anji Liu; Xinzhu Wei; Sriram Sankararaman; Guy Van den Broeck (RECOMB, 2022)

Neuro-symbolic entropy regularization. - Kareem Ahmed; Eric Wang; Kai-Wei Chang; Guy Van den Broeck (UAI, 2022)

Strudel: A fast and accurate learner of structured-decomposable probabilistic circuits. - Meihua Dang; Antonio Vergari; Guy Van den Broeck (Int. J. Approx. Reason., 2022)

On the Tractability of SHAP Explanations. - Guy Van den Broeck; Anton Lykov; Maximilian Schleich; Dan Suciu (J. Artif. Intell. Res., 2022)

Out-of-Distribution Generalization by Neural-Symbolic Joint Training. - Anji Liu; Hongming Xu 003; Guy Van den Broeck; Yitao Liang (AAAI, 2023)

Certifying Fairness of Probabilistic Circuits. - Nikil Roashan Selvam; Guy Van den Broeck; YooJung Choi 001 (AAAI, 2023)

Semantic Strengthening of Neuro-Symbolic Learning. - Kareem Ahmed; Kai-Wei Chang; Guy Van den Broeck (AISTATS, 2023)

Mixtures of All Trees. - Nikil Roashan Selvam; Honghua Zhang; Guy Van den Broeck (AISTATS, 2023)

SIMPLE: A Gradient Estimator for k-Subset Sampling. - Kareem Ahmed; Zhe Zeng; Mathias Niepert; Guy Van den Broeck (ICLR, 2023)

Scaling Up Probabilistic Circuits by Latent Variable Distillation. - Anji Liu; Honghua Zhang; Guy Van den Broeck (ICLR, 2023)

Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits. - Xuejie Liu; Anji Liu; Guy Van den Broeck; Yitao Liang (ICML, 2023)

Tractable Control for Autoregressive Language Generation. - Honghua Zhang; Meihua Dang; Nanyun Peng 001; Guy Van den Broeck (ICML, 2023)

On the Paradox of Learning to Reason from Data. - Honghua Zhang; Liunian Harold Li; Tao Meng; Kai-Wei Chang; Guy Van den Broeck (IJCAI, 2023)

A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints. - Kareem Ahmed; Kai-Wei Chang; Guy Van den Broeck (NeurIPS, 2023)

A Unified Approach to Count-Based Weakly Supervised Learning. - Vinay Shukla; Zhe Zeng; Kareem Ahmed; Guy Van den Broeck (NeurIPS, 2023)

Collapsed Inference for Bayesian Deep Learning. - Zhe Zeng; Guy Van den Broeck (NeurIPS, 2023)

Scaling integer arithmetic in probabilistic programs. - William X. Cao; Poorva Garg; Ryan Tjoa; Steven Holtzen; Todd D. Millstein; Guy Van den Broeck (UAI, 2023)

High Dimensional Causal Inference with Variational Backdoor Adjustment. - Daniel Israel; Aditya Grover; Guy Van den Broeck (CoRR, 2023)

Expressive Modeling Is Insufficient for Offline RL: A Tractable Inference Perspective. - Xuejie Liu; Anji Liu; Guy Van den Broeck; Yitao Liang (CoRR, 2023)

Scalable Analysis of Probabilistic Models and Programs (Dagstuhl Seminar 23241). - Sebastian Junges; Joost-Pieter Katoen; Scott Sanner; Guy Van den Broeck; Bahare Salmani (Dagstuhl Reports, 2023)

Semantic Loss Functions for Neuro-Symbolic Structured Prediction. - 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 (Compendium of Neurosymbolic Artificial Intelligence, 2023)

Where is the signal in tokenization space? - Renato Lui Geh; Honghua Zhang; Kareem Ahmed; Benjie Wang 001; Guy Van den Broeck (EMNLP, 2024)

Image Inpainting via Tractable Steering of Diffusion Models. - Anji Liu; Mathias Niepert; Guy Van den Broeck (ICLR, 2024)

Probabilistically Rewired Message-Passing Neural Networks. - Chendi Qian; Andrei Manolache; Kareem Ahmed; Zhe Zeng; Guy Van den Broeck; Mathias Niepert; Christopher Morris 001 (ICLR, 2024)

Scaling Tractable Probabilistic Circuits: A Systems Perspective. - Anji Liu; Kareem Ahmed; Guy Van den Broeck (ICML, 2024)

A Compositional Atlas for Algebraic Circuits. - Benjie Wang 001; Denis Deratani Mauá; Guy Van den Broeck; YooJung Choi 001 (NeurIPS, 2024)

A Tractable Inference Perspective of Offline RL. - Xuejie Liu; Anji Liu; Guy Van den Broeck; Yitao Liang (NeurIPS, 2024)

Adaptable Logical Control for Large Language Models. - Honghua Zhang; Po-Nien Kung; Masahiro Yoshida; Guy Van den Broeck; Nanyun Peng 001 (NeurIPS, 2024)

Polynomial Semantics of Tractable Probabilistic Circuits. - Oliver Broadrick; Honghua Zhang; Guy Van den Broeck (UAI, 2024)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (CoRR, 2024)

A Circus of Circuits: Connections Between Decision Diagrams, Circuits, and Automata. - Antoine Amarilli; Marcelo Arenas; YooJung Choi 001; Mikaël Monet; Guy Van den Broeck; Benjie Wang 001 (CoRR, 2024)

Semantic Loss Functions for Neuro-Symbolic Structured Prediction. - 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 (CoRR, 2024)

Learning to Discretize Denoising Diffusion ODEs. - Vinh Tong; Anji Liu; Trung-Dung Hoang; Guy Van den Broeck; Mathias Niepert (CoRR, 2024)

Probabilistic Circuits for Cumulative Distribution Functions. - Oliver Broadrick; William X. Cao; Benjie Wang 001; Martin Trapp 001; Guy Van den Broeck (CoRR, 2024)

Bit Blasting Probabilistic Programs. - Poorva Garg; Steven Holtzen; Guy Van den Broeck; Todd D. Millstein (Proc. ACM Program. Lang., 2024)

On the Relationship Between Monotone and Squared Probabilistic Circuits. - Benjie Wang 001; Guy Van den Broeck (AAAI, 2025)

Adversarial Tokenization. - Renato Lui Geh; Zilei Shao; Guy Van den Broeck (ACL (1), 2025)

Restructuring Tractable Probabilistic Circuits. - Honghua Zhang; Benjie Wang 001; Marcelo Arenas; Guy Van den Broeck (AISTATS, 2025)

Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models. - Siyan Zhao; Daniel Israel; Guy Van den Broeck; Aditya Grover (AISTATS, 2025)

Enhancing and Evaluating Probabilistic Circuits for High-Resolution Lossless Image Compression. - Daniel Severo 001; Jingtong Su; Anji Liu; Jeff Johnson; Brian Karrer; Guy Van den Broeck; Matthew J. Muckley; Karen Ullrich (DCC, 2025)

Controllable Generation via Locally Constrained Resampling. - Kareem Ahmed; Kai-Wei Chang; Guy Van den Broeck (ICLR, 2025)

Discrete Copula Diffusion. - Anji Liu; Oliver Broadrick; Mathias Niepert; Guy Van den Broeck (ICLR, 2025)

Learning to Discretize Denoising Diffusion ODEs. - Vinh Tong; Dung-Trung Hoang; Anji Liu; Guy Van den Broeck; Mathias Niepert (ICLR, 2025)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Clara Meister; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; et al. (Trans. Mach. Learn. Res., 2025)

Deep Generative Models with Hard Linear Equality Constraints. - Ruoyan Li; Dipti Ranjan Sahu; Guy Van den Broeck; Zhe Zeng (CoRR, 2025)

Enabling Autoregressive Models to Fill In Masked Tokens. - Daniel Israel; Aditya Grover; Guy Van den Broeck (CoRR, 2025)

Tractable Transformers for Flexible Conditional Generation. - Anji Liu; Xuejie Liu; Dayuan Zhao; Mathias Niepert; Yitao Liang; Guy Van den Broeck (CoRR, 2025)

Symmetry-Preserving Diffusion Models via Target Symmetrization. - Vinh Tong; Yun Ye; Trung-Dung Hoang; Anji Liu; Guy Van den Broeck; Mathias Niepert (CoRR, 2025)

TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation. - Gwen Yidou Weng; Benjie Wang 001; Guy Van den Broeck (CoRR, 2025)

Plug-and-Play Context Feature Reuse for Efficient Masked Generation. - Xuejie Liu; Anji Liu; Guy Van den Broeck; Yitao Liang (CoRR, 2025)

Rethinking Probabilistic Circuit Parameter Learning. - Anji Liu; Guy Van den Broeck (CoRR, 2025)

Accelerating Diffusion LLMs via Adaptive Parallel Decoding. - Daniel Israel; Guy Van den Broeck; Aditya Grover (CoRR, 2025)

The Limits of Tractable Marginalization. - Oliver Broadrick; Sanyam Agarwal; Guy Van den Broeck; Markus Bläser (CoRR, 2025)

Scaling Probabilistic Circuits via Monarch Matrices. - Honghua Zhang; Meihua Dang; Benjie Wang 001; Stefano Ermon; Nanyun Peng 001; Guy Van den Broeck (CoRR, 2025)

Zero-Variance Gradients for Variational Autoencoders. - Zilei Shao; Anji Liu; Guy Van den Broeck (CoRR, 2025)

Tuning Random Generators: Property-Based Testing as Probabilistic Programming. - Ryan Tjoa; Poorva Garg; Harrison Goldstein; Todd D. Millstein; Benjamin C. Pierce; Guy Van den Broeck (CoRR, 2025)