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Sampling

101 papers

Papers per year

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Papers

On Testing of Samplers NIPS 2020
Neutralizing Self-Selection Bias in Sampling for Sortition NIPS 2020
Sampling from a k-DPP without looking at all items NIPS 2020
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method NIPS 2020
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration NIPS 2020
Adaptive Shrinkage Estimation for Streaming Graphs NIPS 2020
Recovery of sparse linear classifiers from mixture of responses NIPS 2020
On Importance Sampling-Based Evaluation of Latent Language Models ACL 2020
Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection CVPR 2020
Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors EMNLP 2020
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries NIPS 2019
Random Tessellation Forests NIPS 2019
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates NIPS 2019
Approximating the Permanent by Sampling from Adaptive Partitions NIPS 2019
A General Framework for Symmetric Property Estimation NIPS 2019
DppNet: Approximating Determinantal Point Processes with Deep Networks NIPS 2019
Exact sampling of determinantal point processes with sublinear time preprocessing NIPS 2019
On Testing of Uniform Samplers AAAI 2019
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems EMNLP 2019
Determinantal Point Processes for Coresets JMLR 2019
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation NIPS 2018
Geometrically Coupled Monte Carlo Sampling NIPS 2018
Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo NIPS 2018
Sampling Informative Training Data for RNN Language Models ACL 2018
Polynomial time algorithms for dual volume sampling NIPS 2017