Probability

Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling JMLR 2024 Measuring Sample Quality in Algorithms for Intractable Normalizing Function Problems JMLR 2024 Minimum Entropy Coupling with Bottleneck NeurIPS 2024 Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes AISTATS 2024 On cyclical MCMC sampling AISTATS 2024 Online Algorithms with Uncertainty-Quantified Predictions ICML 2024 Online Resource Allocation with Non-Stationary Customers ICML 2024 Online Sampling and Decision Making with Low Entropy IJCAI 2024 On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models NeurIPS 2024 On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling JMLR 2024 On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation AISTATS 2024 Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions JMLR 2024 Parallel-in-Time Probabilistic Numerical ODE Solvers JMLR 2024 Parametric model reduction of mean-field and stochastic systems via higher-order action matching NeurIPS 2024 Pareto Smoothed Importance Sampling JMLR 2024 PASOA- PArticle baSed Bayesian Optimal Adaptive design ICML 2024 Piecewise deterministic generative models NeurIPS 2024 Probabilistic size-and-shape functional mixed models NeurIPS 2024 Probabilities of Causation with Nonbinary Treatment and Effect AAAI 2024 Quasi-Bayes meets Vines NeurIPS 2024 Quasi-Monte Carlo Features for Kernel Approximation ICML 2024 Queueing Matching Bandits with Preference Feedback NeurIPS 2024 Random Scaling and Momentum for Non-smooth Non-convex Optimization ICML 2024 Resolution Limit of Single-Photon LiDAR CVPR 2024 Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models NeurIPS 2024