aistats

For deep RL and the future of AI.

Download papers and supplemental materials from open-access paper website, such as AAAI, AAMAS, AISTATS, COLT, CORL, CVPR, ECCV, ICCV, ICLR, ICML, IJCAI, JMLR, NIPS, RSS, WACV.

This is a collection of papers and other resources related to fairness.

A near-optimal exact sampler for discrete probability distributions

AISTATS 2019: Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features

Spectral Tensor Train Parameterization of Deep Learning Layers

AISTATS 2019: Lovász Convolutional Networks

PyTorch implementation for " Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference" (https://arxiv.org/abs/1810.02555).

Repo to explore papers submitted to AISTATS, COLT, ML4H, AAAI, ICML, ICLR, NEURIPS, arXiv, biorxiv, medrxiv

Code for the paper Learning Visual-Semantic Subspace Representations

April 13-15, Virtual Conference

This is the Code and Data repository the RamPINN AISTATS 2026 publication. It shows a physics-informed CARS to Raman recovery strategy without needin the NRB.

DPE code - Code used in "Optimal Algorithms for Multiplayer Multi-Armed Bandits" (AISTATS 2020)

The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions (Experiments)

Code for our AISTATS '22 paper: Improving Attribution Methods by Learning Submodular Functions.

Training Implicit Generative Models via an Invariant statistical loss (ISL)

A website to help see stats & info from different AI models.