posterior sampling

A-NICE-MC: Adversarial Training for MCMC NeurIPS 2017 Differential Privacy for Bayesian Inference through Posterior Sampling JMLR 2017 Optimistic posterior sampling for reinforcement learning: worst-case regret bounds NeurIPS 2017 Post-Inference Prior Swapping ICML 2017 Renyi Differential Privacy Mechanisms for Posterior Sampling NeurIPS 2017 Stochastic Bouncy Particle Sampler ICML 2017 Thompson Sampling for Linear-Quadratic Control Problems AISTATS 2017 Why is Posterior Sampling Better than Optimism for Reinforcement Learning? ICML 2017 Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization AISTATS 2016 Measuring the reliability of MCMC inference with bidirectional Monte Carlo NeurIPS 2016 Sampling for Bayesian Program Learning NeurIPS 2016 Stochastic Quasi-Newton Langevin Monte Carlo ICML 2016 Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors ICML 2016 Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling JMLR 2016 A hybrid sampler for Poisson-Kingman mixture models NeurIPS 2015 Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) ICML 2015 Finite-Time Analysis of Projected Langevin Monte Carlo NeurIPS 2015 Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo ICML 2015 WASP: Scalable Bayes via barycenters of subset posteriors AISTATS 2015 A Level-set Hit-and-run Sampler for Quasi-Concave Distributions AISTATS 2014 Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget ICML 2014 Distributed Bayesian Posterior Sampling via Moment Sharing NeurIPS 2014 Fast Sampling-Based Inference in Balanced Neuronal Networks NeurIPS 2014 Model-based Reinforcement Learning and the Eluder Dimension NeurIPS 2014 Near-optimal Reinforcement Learning in Factored MDPs NeurIPS 2014