posterior sampling

On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling and Beyond NeurIPS 2023 Posterior sampling-based online learning for the stochastic shortest path model UAI 2023 Posterior Sampling for Competitive RL: Function Approximation and Partial Observation NeurIPS 2023 Posterior Sampling for Deep Reinforcement Learning ICML 2023 Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation NeurIPS 2023 Posterior Tracking Algorithm for Classification Bandits AISTATS 2023 Quantized Compressed Sensing with Score-Based Generative Models ICLR 2023 Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent NeurIPS 2023 Score-Based Diffusion Models as Principled Priors for Inverse Imaging ICCV 2023 Self-Correcting Bayesian Optimization through Bayesian Active Learning NeurIPS 2023 Solving Inverse Physics Problems with Score Matching NeurIPS 2023 Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models NeurIPS 2023 Thompson Sampling with Diffusion Generative Prior ICML 2023 Thompson Sampling with Less Exploration is Fast and Optimal ICML 2023 Training Chain-of-Thought via Latent-Variable Inference NeurIPS 2023 Variational Inference for Neyman-Scott Processes AISTATS 2023 An Efficient Sampling Algorithm for Non-smooth Composite Potentials JMLR 2022 A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games ICML 2022 Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning NeurIPS 2022 Deep Neyman-Scott Processes AISTATS 2022 Denoising Diffusion Restoration Models NeurIPS 2022 HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning ICLR 2022 Improving Bayesian Neural Networks by Adversarial Sampling AAAI 2022 Interacting Contour Stochastic Gradient Langevin Dynamics ICLR 2022 Langevin Autoencoders for Learning Deep Latent Variable Models NeurIPS 2022