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07/25/2023
Scaling Integer Arithmetic in Probabilistic Programs
Distributions on integers are ubiquitous in probabilistic modeling but r...
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06/16/2023
Collapsed Inference for Bayesian Deep Learning
Bayesian neural networks (BNNs) provide a formalism to quantify and cali...
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04/15/2023
Tractable Control for Autoregressive Language Generation
Despite the success of autoregressive large language models in text gene...
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02/28/2023
Semantic Strengthening of Neuro-Symbolic Learning
Numerous neuro-symbolic approaches have recently been proposed typically...
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02/27/2023
Mixtures of All Trees
Tree-shaped graphical models are widely used for their tractability. How...
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02/16/2023
Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits
Probabilistic Circuits (PCs) are a general and unified computational fra...
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12/05/2022
Certifying Fairness of Probabilistic Circuits
With the increased use of machine learning systems for decision making, ...
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11/22/2022
Sparse Probabilistic Circuits via Pruning and Growing
Probabilistic circuits (PCs) are a tractable representation of probabili...
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10/10/2022
Scaling Up Probabilistic Circuits by Latent Variable Distillation
Probabilistic Circuits (PCs) are a unified framework for tractable proba...
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10/04/2022
SIMPLE: A Gradient Estimator for k-Subset Sampling
k-subset sampling is ubiquitous in machine learning, enabling regulariza...
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06/01/2022
Semantic Probabilistic Layers for Neuro-Symbolic Learning
We design a predictive layer for structured-output prediction (SOP) that...
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05/23/2022
On the Paradox of Learning to Reason from Data
Logical reasoning is needed in a wide range of NLP tasks. Can a BERT mod...
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01/25/2022
Neuro-Symbolic Entropy Regularization
In structured prediction, the goal is to jointly predict many output var...
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11/23/2021
Lossless Compression with Probabilistic Circuits
Despite extensive progress on image generation, deep generative models a...
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11/08/2021
Solving Marginal MAP Exactly by Probabilistic Circuit Transformations
Probabilistic circuits (PCs) are a class of tractable probabilistic mode...
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10/19/2021
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs
Probabilistic programming is emerging as a popular and effective means o...
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07/16/2021
Towards an Interpretable Latent Space in Structured Models for Video Prediction
We focus on the task of future frame prediction in video governed by und...
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06/04/2021
Tractable Regularization of Probabilistic Circuits
Probabilistic Circuits (PCs) are a promising avenue for probabilistic mo...
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05/26/2021
Model Checking Finite-Horizon Markov Chains with Probabilistic Inference
We revisit the symbolic verification of Markov chains with respect to fi...
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05/21/2021
Probabilistic Sufficient Explanations
Understanding the behavior of learned classifiers is an important task, ...
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03/20/2021
Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction
We study the problem of entity-relation extraction in the presence of sy...
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02/21/2021
Tractable Computation of Expected Kernels by Circuits
Computing the expectation of some kernel function is ubiquitous in machi...
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02/19/2021
Probabilistic Generating Circuits
Generating functions, which are widely used in combinatorics and probabi...
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02/11/2021
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries
Circuit representations are becoming the lingua franca to express and re...
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09/18/2020
Group Fairness by Probabilistic Modeling with Latent Fair Decisions
Machine learning systems are increasingly being used to make impactful d...
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09/18/2020
On the Tractability of SHAP Explanations
SHAP explanations are a popular feature-attribution mechanism for explai...
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07/18/2020
Strudel: Learning Structured-Decomposable Probabilistic Circuits
Probabilistic circuits (PCs) represent a probability distribution as a c...
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06/29/2020
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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06/26/2020
On the Relationship Between Probabilistic Circuits and Determinantal Point Processes
Scaling probabilistic models to large realistic problems and datasets is...
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06/16/2020
Counterexample-Guided Learning of Monotonic Neural Networks
The widespread adoption of deep learning is often attributed to its auto...
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06/15/2020
On Effective Parallelization of Monte Carlo Tree Search
Despite its groundbreaking success in Go and computer games, Monte Carlo...
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05/18/2020
Scaling Exact Inference for Discrete Probabilistic Programs
Probabilistic programming languages (PPLs) are an expressive means of re...
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04/13/2020
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Probabilistic circuits (PCs) are a promising avenue for probabilistic mo...
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02/28/2020
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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02/25/2020
Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration
Off-policy reinforcement learning (RL) is concerned with learning a rewa...
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02/24/2020
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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12/06/2019
LaTeS: Latent Space Distillation for Teacher-Student Driving Policy Learning
We describe a policy learning approach to map visual inputs to driving c...
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10/05/2019
On Tractable Computation of Expected Predictions
Computing expected predictions has many interesting applications in area...
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09/20/2019
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message-Passing
Weighted model integration (WMI) is a very appealing framework for proba...
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06/10/2019
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
As machine learning is increasingly used to make real-world decisions, r...
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06/01/2019
Smoothing Structured Decomposable Circuits
We study the task of smoothing a circuit, i.e., ensuring that all childr...
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04/03/2019
Symbolic Exact Inference for Discrete Probabilistic Programs
The computational burden of probabilistic inference remains a hurdle for...
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03/13/2019
Efficient Search-Based Weighted Model Integration
Weighted model integration (WMI) extends Weighted model counting (WMC) t...
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03/12/2019
Generating and Sampling Orbits for Lifted Probabilistic Inference
Lifted inference scales to large probability models by exploiting symmet...
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03/05/2019
What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features
While discriminative classifiers often yield strong predictive performan...
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02/27/2019
Learning Logistic Circuits
This paper proposes a new classification model called logistic circuits....
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02/27/2019
On Constrained Open-World Probabilistic Databases
Increasing amounts of available data have led to a heightened need for r...
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12/13/2018
Active Inductive Logic Programming for Code Search
Modern search techniques either cannot efficiently incorporate human fee...
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05/31/2018
Approximate Knowledge Compilation by Online Collapsed Importance Sampling
We introduce collapsed compilation, a novel approximate inference algori...
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05/29/2018