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Markov Chain Monte Carlo

142 directly classified papers

Papers per year

Papers

Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo NIPS 2018
The promises and pitfalls of Stochastic Gradient Langevin Dynamics NIPS 2018
Large-Scale Stochastic Sampling from the Probability Simplex NIPS 2018
HOGWILD!-Gibbs can be PanAccurate NIPS 2018
Mental Sampling in Multimodal Representations NIPS 2018
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks NIPS 2017
Stochastic Gradient Monomial Gamma Sampler ICML 2017
A-NICE-MC: Adversarial Training for MCMC NIPS 2017
Collapsed variational Bayes for Markov jump processes NIPS 2017
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling NIPS 2016
Stochastic Gradient MCMC with Stale Gradients NIPS 2016
Stochastic Quasi-Newton Langevin Monte Carlo ICML 2016
Learning Weight Uncertainty With Stochastic Gradient MCMC for Shape Classification CVPR 2016
Measuring the reliability of MCMC inference with bidirectional Monte Carlo NIPS 2016
Synthesis of MCMC and Belief Propagation NIPS 2016
Stochastic Gradient Geodesic MCMC Methods NIPS 2016
Variational Consensus Monte Carlo NIPS 2015
Exponential Integration for Hamiltonian Monte Carlo ICML 2015
Transformation of Markov Random Fields for Marginal Distribution Estimation CVPR 2015
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models NIPS 2015
Measuring Sample Quality with Stein's Method NIPS 2015
MCMC for Variationally Sparse Gaussian Processes NIPS 2015
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling NIPS 2015
Reflection, Refraction, and Hamiltonian Monte Carlo NIPS 2015
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families NIPS 2015