
Tractable Computation of Expected Kernels by Circuits
Computing the expectation of some kernel function is ubiquitous in machi...
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Probabilistic Generating Circuits
Generating functions, which are widely used in combinatorics and probabi...
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A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex InformationTheoretic Queries
Circuit representations are becoming the lingua franca to express and re...
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Group Fairness by Probabilistic Modeling with Latent Fair Decisions
Machine learning systems are increasingly being used to make impactful d...
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On the Tractability of SHAP Explanations
SHAP explanations are a popular featureattribution mechanism for explai...
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Strudel: Learning StructuredDecomposable Probabilistic Circuits
Probabilistic circuits (PCs) represent a probability distribution as a c...
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Handling Missing Data in Decision Trees: A Probabilistic Approach
Decision trees are a popular family of models due to their attractive pr...
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On the Relationship Between Probabilistic Circuits and Determinantal Point Processes
Scaling probabilistic models to large realistic problems and datasets is...
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CounterexampleGuided Learning of Monotonic Neural Networks
The widespread adoption of deep learning is often attributed to its auto...
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On Effective Parallelization of Monte Carlo Tree Search
Despite its groundbreaking success in Go and computer games, Monte Carlo...
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Scaling Exact Inference for Discrete Probabilistic Programs
Probabilistic programming languages (PPLs) are an expressive means of re...
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Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Probabilistic circuits (PCs) are a promising avenue for probabilistic mo...
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Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
Weighted model integration (WMI) is a very appealing framework for proba...
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OffPolicy Deep Reinforcement Learning with Analogous Disentangled Exploration
Offpolicy reinforcement learning (RL) is concerned with learning a rewa...
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Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings
To deal with increasing amounts of uncertainty and incompleteness in rel...
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LaTeS: Latent Space Distillation for TeacherStudent Driving Policy Learning
We describe a policy learning approach to map visual inputs to driving c...
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On Tractable Computation of Expected Predictions
Computing expected predictions has many interesting applications in area...
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Hybrid Probabilistic Inference with Logical Constraints: Tractability and MessagePassing
Weighted model integration (WMI) is a very appealing framework for proba...
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Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
As machine learning is increasingly used to make realworld decisions, r...
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Smoothing Structured Decomposable Circuits
We study the task of smoothing a circuit, i.e., ensuring that all childr...
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Symbolic Exact Inference for Discrete Probabilistic Programs
The computational burden of probabilistic inference remains a hurdle for...
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Efficient SearchBased Weighted Model Integration
Weighted model integration (WMI) extends Weighted model counting (WMC) t...
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Generating and Sampling Orbits for Lifted Probabilistic Inference
Lifted inference scales to large probability models by exploiting symmet...
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What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features
While discriminative classifiers often yield strong predictive performan...
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Learning Logistic Circuits
This paper proposes a new classification model called logistic circuits....
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On Constrained OpenWorld Probabilistic Databases
Increasing amounts of available data have led to a heightened need for r...
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Active Inductive Logic Programming for Code Search
Modern search techniques either cannot efficiently incorporate human fee...
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Approximate Knowledge Compilation by Online Collapsed Importance Sampling
We introduce collapsed compilation, a novel approximate inference algori...
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On Robust Trimming of Bayesian Network Classifiers
This paper considers the problem of removing costly features from a Baye...
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A Semantic Loss Function for Deep Learning with Symbolic Knowledge
This paper develops a novel methodology for using symbolic knowledge in ...
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Domain Recursion for Lifted Inference with Existential Quantifiers
In recent work, we proved that the domain recursion inference rule makes...
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Probabilistic Program Abstractions
Abstraction is a fundamental tool for reasoning about complex systems. P...
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Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification
After being trained, classifiers must often operate on data that has bee...
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New Liftable Classes for FirstOrder Probabilistic Inference
Statistical relational models provide compact encodings of probabilistic...
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Symmetric Weighted FirstOrder Model Counting
The FO Model Counting problem (FOMC) is the following: given a sentence ...
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Lifted Probabilistic Inference for Asymmetric Graphical Models
Lifted probabilistic inference algorithms have been successfully applied...
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Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data
We propose an efficient family of algorithms to learn the parameters of ...
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Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting
In this paper we study lifted inference for the Weighted FirstOrder Mod...
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On the Role of Canonicity in Bottomup Knowledge Compilation
We consider the problem of bottomup compilation of knowledge bases, whi...
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Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference
Exchangeability is a central notion in statistics and probability theory...
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Skolemization for Weighted FirstOrder Model Counting
Firstorder model counting emerged recently as a novel reasoning task, a...
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On the Complexity and Approximation of Binary Evidence in Lifted Inference
Lifted inference algorithms exploit symmetries in probabilistic models t...
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Inference and learning in probabilistic logic programs using weighted Boolean formulas
Probabilistic logic programs are logic programs in which some of the fac...
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Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference
We propose an approach to lifted approximate inference for firstorder p...
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Lifted Variable Elimination: A Novel Operator and Completeness Results
Various methods for lifted probabilistic inference have been proposed, b...
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Inference in Probabilistic Logic Programs using Weighted CNF's
Probabilistic logic programs are logic programs in which some of the fac...
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Guy Van den Broeck
verfied profile
Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the Statistical and Relational Artificial Intelligence (StarAI) lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His work has been recognized with best paper awards from key artificial intelligence venues such as UAI, ILP, KR, and AAAI (honorable mention). He also serves as Associate Editor for the Journal of Artificial Intelligence Research (JAIR). Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI19 Computers and Thought Award.