Guy Van den Broeck

Orcid: 0000-0003-3434-2503

Affiliations:
  • University of California, Los Angeles, Computer Science Department


According to our database1, Guy Van den Broeck authored at least 162 papers between 2009 and 2024.

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Bibliography

2024
Bit Blasting Probabilistic Programs.
Proc. ACM Program. Lang., 2024

Controllable Generation via Locally Constrained Resampling.
CoRR, 2024

Discrete Copula Diffusion.
CoRR, 2024

Probabilistic Circuits for Cumulative Distribution Functions.
CoRR, 2024

On the Relationship Between Monotone and Squared Probabilistic Circuits.
CoRR, 2024

Adaptable Logical Control for Large Language Models.
CoRR, 2024

Learning to Discretize Denoising Diffusion ODEs.
CoRR, 2024

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

A Circus of Circuits: Connections Between Decision Diagrams, Circuits, and Automata.
CoRR, 2024

Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models.
CoRR, 2024

On the Challenges and Opportunities in Generative AI.
CoRR, 2024

Polynomial Semantics of Tractable Probabilistic Circuits.
CoRR, 2024

Scaling Tractable Probabilistic Circuits: A Systems Perspective.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Probabilistically Rewired Message-Passing Neural Networks.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Image Inpainting via Tractable Steering of Diffusion Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Where is the signal in tokenization space?
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

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

Scalable Analysis of Probabilistic Models and Programs (Dagstuhl Seminar 23241).
Dagstuhl Reports, 2023

Expressive Modeling Is Insufficient for Offline RL: A Tractable Inference Perspective.
CoRR, 2023

High Dimensional Causal Inference with Variational Backdoor Adjustment.
CoRR, 2023

Scaling integer arithmetic in probabilistic programs.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Collapsed Inference for Bayesian Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Unified Approach to Count-Based Weakly Supervised Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Paradox of Learning to Reason from Data.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Tractable Control for Autoregressive Language Generation.
Proceedings of the International Conference on Machine Learning, 2023

Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits.
Proceedings of the International Conference on Machine Learning, 2023

Scaling Up Probabilistic Circuits by Latent Variable Distillation.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

SIMPLE: A Gradient Estimator for k-Subset Sampling.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Mixtures of All Trees.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Semantic Strengthening of Neuro-Symbolic Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Certifying Fairness of Probabilistic Circuits.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Out-of-Distribution Generalization by Neural-Symbolic Joint Training.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
On the Tractability of SHAP Explanations.
J. Artif. Intell. Res., 2022

Strudel: A fast and accurate learner of structured-decomposable probabilistic circuits.
Int. J. Approx. Reason., 2022

Neuro-symbolic entropy regularization.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Tractable and Expressive Generative Models of Genetic Variation Data.
Proceedings of the Research in Computational Molecular Biology, 2022

Sparse Probabilistic Circuits via Pruning and Growing.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Semantic Probabilistic Layers for Neuro-Symbolic Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Lossless Compression with Probabilistic Circuits.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Solving Marginal MAP Exactly by Probabilistic Circuit Transformations.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

PYLON: A PyTorch Framework for Learning with Constraints.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Experiments for 'Model Checking Finite-Horizon Markov Chains with Probabilistic Inference'.
Dataset, April, 2021

flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs.
CoRR, 2021

Towards an Interpretable Latent Space in Structured Models for Video Prediction.
CoRR, 2021

Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction.
CoRR, 2021

Tractable Computation of Expected Kernels by Circuits.
CoRR, 2021

A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries.
CoRR, 2021

Open-world probabilistic databases: Semantics, algorithms, complexity.
Artif. Intell., 2021

Tractable computation of expected kernels.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Tractable Regularization of Probabilistic Circuits.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Probabilistic Sufficient Explanations.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Probabilistic Generating Circuits.
Proceedings of the 38th International Conference on Machine Learning, 2021

From Probabilistic Circuits to Probabilistic Programs and Back.
Proceedings of the 13th International Conference on Agents and Artificial Intelligence, 2021

Model Checking Finite-Horizon Markov Chains with Probabilistic Inference.
Proceedings of the Computer Aided Verification - 33rd International Conference, 2021

Logical abstractions for noisy variational Quantum algorithm simulation.
Proceedings of the ASPLOS '21: 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2021

Juice: A Julia Package for Logic and Probabilistic Circuits.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Group Fairness by Probabilistic Modeling with Latent Fair Decisions.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Scaling exact inference for discrete probabilistic programs.
Proc. ACM Program. Lang., 2020

Semantic and Generalized Entropy Loss Functions for Semi-Supervised Deep Learning.
Entropy, 2020

SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091).
Dagstuhl Reports, 2020

Handling Missing Data in Decision Trees: A Probabilistic Approach.
CoRR, 2020

On Effective Parallelization of Monte Carlo Tree Search.
CoRR, 2020

Dice: Compiling Discrete Probabilistic Programs for Scalable Inference.
CoRR, 2020

On the Relationship Between Probabilistic Circuits and Determinantal Point Processes.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Strudel: Learning Structured-Decomposable Probabilistic Circuits.
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Counterexample-Guided Learning of Monotonic Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams.
Proceedings of the Advances in Intelligent Data Analysis XVIII, 2020

Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing.
Proceedings of the 37th International Conference on Machine Learning, 2020

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits.
Proceedings of the 37th International Conference on Machine Learning, 2020

SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning.
Proceedings of the 4th Conference on Robot Learning, 2020

Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration.
Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
LaTeS: Latent Space Distillation for Teacher-Student Driving Policy Learning.
CoRR, 2019

Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing.
CoRR, 2019

Symbolic Exact Inference for Discrete Probabilistic Programs.
CoRR, 2019

Efficient Search-Based Weighted Model Integration.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Generating and Sampling Orbits for Lifted Probabilistic Inference.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Smoothing Structured Decomposable Circuits.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Towards Hardware-Aware Tractable Learning of Probabilistic Models.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On Hardware-Aware Probabilistic Frameworks for Resource Constrained Embedded Applications.
Proceedings of the Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing, 2019

On Tractable Computation of Expected Predictions.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Active inductive logic programming for code search.
Proceedings of the 41st International Conference on Software Engineering, 2019

Scalable Rule Learning in Probabilistic Knowledge Bases.
Proceedings of the 1st Conference on Automated Knowledge Base Construction, 2019

On Constrained Open-World Probabilistic Databases.
Proceedings of the 1st Conference on Automated Knowledge Base Construction, 2019

Learning Logistic Circuits.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Approximate Knowledge Compilation by Online Collapsed Importance Sampling.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On Robust Trimming of Bayesian Network Classifiers.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

A Semantic Loss Function for Deep Learning with Symbolic Knowledge.
Proceedings of the 35th International Conference on Machine Learning, 2018

Sound Abstraction and Decomposition of Probabilistic Programs.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Algebraic model counting.
J. Appl. Log., 2017

Query Processing on Probabilistic Data: A Survey.
Found. Trends Databases, 2017

Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification.
CoRR, 2017

Domain Recursion for Lifted Inference with Existential Quantifiers.
CoRR, 2017

Learning the Structure of Probabilistic Sentential Decision Diagrams.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Probabilistic Program Abstractions.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Optimal Feature Selection for Decision Robustness in Bayesian Networks.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Open-World Probabilistic Databases: An Abridged Report.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Combining Stochastic Constraint Optimization and Probabilistic Programming - From Knowledge Compilation to Constraint Solving.
Proceedings of the Principles and Practice of Constraint Programming, 2017

Coded machine learning: Joint informed replication and learning for linear regression.
Proceedings of the 55th Annual Allerton Conference on Communication, 2017

2016
Lifted generative learning of Markov logic networks.
Mach. Learn., 2016

T<sub>P</sub>-Compilation for inference in probabilistic logic programs.
Int. J. Approx. Reason., 2016

Exploiting local and repeated structure in Dynamic Bayesian Networks.
Artif. Intell., 2016

Quantifying Causal Effects on Query Answering in Databases.
Proceedings of the 8th USENIX Workshop on the Theory and Practice of Provenance, 2016

New Liftable Classes for First-Order Probabilistic Inference.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Open-World Probabilistic Databases.
Proceedings of the Principles of Knowledge Representation and Reasoning: Proceedings of the Fifteenth International Conference, 2016

First-Order Model Counting in a Nutshell.
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report.
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Open World Probabilistic Databases (Extended Abstract).
Proceedings of the 29th International Workshop on Description Logics, 2016

Robust channel coding strategies for machine learning data.
Proceedings of the 54th Annual Allerton Conference on Communication, 2016

Component Caching in Hybrid Domains with Piecewise Polynomial Densities.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Inference and learning in probabilistic logic programs using weighted Boolean formulas.
Theory Pract. Log. Program., 2015

Knowledge compilation of logic programs using approximation fixpoint theory.
Theory Pract. Log. Program., 2015

Reports of the AAAI 2014 Conference Workshops.
AI Mag., 2015

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Hashing-Based Approximate Probabilistic Inference in Hybrid Domains.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Symmetric Weighted First-Order Model Counting.
Proceedings of the 34th ACM Symposium on Principles of Database Systems, 2015

ProbLog2: Probabilistic Logic Programming.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Tractable Learning for Complex Probability Queries.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Anytime Inference in Probabilistic Logic Programs with Tp-Compilation.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Inducing Probabilistic Relational Rules from Probabilistic Examples.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Tractable Learning for Structured Probability Spaces: A Case Study in Learning Preference Distributions.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Probabilistic Inference in Hybrid Domains by Weighted Model Integration.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Symmetry in Probabilistic Databases.
Proceedings of the 9th Alberto Mendelzon International Workshop on Foundations of Data Management, Lima, Peru, May 6, 2015

Probability Distributions over Structured Spaces.
Proceedings of the 2015 AAAI Spring Symposia, 2015

Towards High-Level Probabilistic Reasoning with Lifted Inference.
Proceedings of the 2015 AAAI Spring Symposia, 2015

Lifted Probabilistic Inference for Asymmetric Graphical Models.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

On the Role of Canonicity in Knowledge Compilation.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Lifted Probabilistic Inference: A Guide for the Database Researcher.
IEEE Data Eng. Bull., 2014

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data.
CoRR, 2014

On the Role of Canonicity in Bottom-up Knowledge Compilation.
CoRR, 2014

Probabilistic Sentential Decision Diagrams.
Proceedings of the Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, 2014

Skolemization for Weighted First-Order Model Counting.
Proceedings of the Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, 2014

Efficient Probabilistic Inference for Dynamic Relational Models.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

Preface.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

2013
Lifted Inference and Learning in Statistical Relational Models (Eerste-orde inferentie en leren in statistische relationele modellen).
PhD thesis, 2013

On the Complexity and Approximation of Binary Evidence in Lifted Inference.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Completeness Results for Lifted Variable Elimination.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

On the Completeness of Lifted Variable Elimination.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

Lifted Generative Parameter Learning.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

On the Complexity and Approximation of Binary Evidence in Lifted Inference.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

2012
k-Optimal: a novel approximate inference algorithm for ProbLog.
Mach. Learn., 2012

Lifted Variable Elimination: A Novel Operator and Completeness Results
CoRR, 2012

Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

A Particle Filter for Probabilistic Dynamic Relational Domains.
Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

Liftability of Probabilistic Inference: Upper and Lower Bounds.
Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference.
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011
Inference in Probabilistic Logic Programs using Weighted CNF's.
Proceedings of the UAI 2011, 2011

On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Relational Learning for Football-Related Predictions.
Proceedings of the Latest Advances in Inductive Logic Programming, 2011

Lifted Probabilistic Inference by First-Order Knowledge Compilation.
Proceedings of the IJCAI 2011, 2011

An Algebraic Prolog for Reasoning about Possible Worlds.
Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

2010
Probabilistic Programming for Planning Problems.
Proceedings of the Statistical Relational Artificial Intelligence, 2010

DTProbLog: A Decision-Theoretic Probabilistic Prolog.
Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

2009
Monte-Carlo Tree Search in Poker Using Expected Reward Distributions.
Proceedings of the Advances in Machine Learning, 2009


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