UCLA-StarAI/CountLoss

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Created about 1 year ago
Updated 12 months ago

UCLA-StarAI/CountLoss

UCLA-StarAI

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Semantic-Loss

Semantic Loss code

LogisticCircuit

Logistic Circuits

LearnPSDD

Forclift

First-order knowledge compilation for lifted probabilistic inference

SparsePC

SIMPLE

SIMPLE: A Gradient Estimator for $k$-subset Sampling

Jupyter Notebook

mc2

Code for "On Tractable Computation of Expected Predictions, NeurIPS 2019"

Circuit-Model-Zoo

A collection of logical, probabilistic, and logistic circuits

circuit-ops-atlas

Jupyter Notebook

Semantic-Strengthening

LearnFairNB

Jupyter Notebook

GeLaTo

NaCL

Naive Conformant Learning

Jupyter Notebook

FairPC.jl

Group Fairness by Probabilistic Modeling with Latent Fair Decisions

MoAT

Mixture of All Trees

Collapsed-Compilation

Collapsed compilation code

mpwmi

Code for ICML2020 paper "Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing"

recoin

Code for NeurIPS 2020 paper "Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations"

Tractable-PC-Regularization

Code of the NeurIPS 2021 paper "Tractable Regularization of Probabilistic Circuits"

Jupyter Notebook

Strudel

pysmi

proof-of-concept experiments for UAI19 paper "Efficient Search-based Weighted Model Integration"

ProbabilisticSufficientExplanations

CIBER

Collapsed Inference for Bayesian Deep Learning

TrimBN

PC-DiscriminationPatterns

LearnSDD

Tractable learning of probability distributions with Sentential Decision Diagrams

Analogous-Disentangled-Actor-Critic

Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration

ScalaDD

Experimental implementation of decision diagrams (OBDDs, SDDs, PSDDs) in Scala.

DensityEstimationDatasets.jl

genetic-pc

Jupyter Notebook

NeSyEntropy

Jupyter Notebook

FO-CNF

This document describes a file format called FO-CNF for representing function-free first-order conjunctive normal form theories.

Py-Psdd

LearnPsdd implemented in Python

PseudoSL

Direct-Factored-Deletion

Prototype implementation of direct and factored deletion algorithms to learn Bayesian network parameters from incomplete data under the MCAR and MAR assumptions. These algorithms are consistent, yet they only require a single pass over the data, and no inference in the Bayesian network.

Tiramisu