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Sampling

52 directly classified papers

Papers per year

Papers

Stein Self-Repulsive Dynamics: Benefits From Past Samples NIPS 2020
Gradient Estimation with Stochastic Softmax Tricks NIPS 2020
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method NIPS 2020
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates NIPS 2019
Determinantal Point Processes for Coresets JMLR 2019
Exact sampling of determinantal point processes with sublinear time preprocessing NIPS 2019
DppNet: Approximating Determinantal Point Processes with Deep Networks NIPS 2019
A General Framework for Symmetric Property Estimation NIPS 2019
Approximating the Permanent by Sampling from Adaptive Partitions NIPS 2019
Polynomial time algorithms for dual volume sampling NIPS 2017
Learning Determinantal Point Processes with Moments and Cycles ICML 2017
Kronecker Determinantal Point Processes NIPS 2016
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling NIPS 2016
Fast Bidirectional Probability Estimation in Markov Models NIPS 2015
Neural Adaptive Sequential Monte Carlo NIPS 2015
Approximate Slice Sampling for Bayesian Posterior Inference AISTATS 2014
What do row and column marginals reveal about your dataset? NIPS 2013
Fast Determinantal Point Process Sampling with Application to Clustering NIPS 2013
Embed and Project: Discrete Sampling with Universal Hashing NIPS 2013
Efficient Sampling for Bipartite Matching Problems NIPS 2012
Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction NIPS 2012
Accelerated Adaptive Markov Chain for Partition Function Computation NIPS 2011
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC NIPS 2011
Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning NIPS 2010
Particle-based Variational Inference for Continuous Systems NIPS 2009