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For deep RL and the future of AI.
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).
Code for the paper Learning Visual-Semantic Subspace Representations
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)
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.
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