Sampling

Kernel Thinning COLT 2021 Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm COLT 2021 Query complexity of least absolute deviation regression via robust uniform convergence COLT 2021 Sampling with Trusthworthy Constraints: A Variational Gradient Framework NeurIPS 2021 A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions NeurIPS 2020 Adaptive Shrinkage Estimation for Streaming Graphs NeurIPS 2020 Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection CVPR 2020 Gradient Estimation with Stochastic Softmax Tricks NeurIPS 2020 Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration NeurIPS 2020 Neutralizing Self-Selection Bias in Sampling for Sortition NeurIPS 2020 On Importance Sampling-Based Evaluation of Latent Language Models ACL 2020 On Testing of Samplers NeurIPS 2020 On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method NeurIPS 2020 Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm NeurIPS 2020 Recovery of sparse linear classifiers from mixture of responses NeurIPS 2020 Sampling from a k-DPP without looking at all items NeurIPS 2020 Stein Self-Repulsive Dynamics: Benefits From Past Samples NeurIPS 2020 Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors EMNLP 2020 A General Framework for Symmetric Property Estimation NeurIPS 2019 Approximating the Permanent by Sampling from Adaptive Partitions NeurIPS 2019 Determinantal Point Processes for Coresets JMLR 2019 Discriminator Rejection Sampling ICLR 2019 DppNet: Approximating Determinantal Point Processes with Deep Networks NeurIPS 2019 Efficient Augmentation via Data Subsampling ICLR 2019 Efficient Diversified Mini-Batch Selection using Variable High-layer Features ACML 2019