Stochastic Processes

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 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 Small coresets via negative dependence: DPPs, linear statistics, and concentration NeurIPS 2024 Spectral Learning of Shared Dynamics Between Generalized-Linear Processes NeurIPS 2024 Statistical Spatially Inhomogeneous Diffusion Inference AAAI 2024 Stochastic Gradient Flow Dynamics of Test Risk and its Exact Solution for Weak Features ICML 2024 Stochastic Localization via Iterative Posterior Sampling ICML 2024 Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent JMLR 2024 Testing exchangeability by pairwise betting AISTATS 2024 The Expected Loss of Preconditioned Langevin Dynamics Reveals the Hessian Rank AAAI 2024 Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States UAI 2024 Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data ICML 2024 Understanding Stochastic Natural Gradient Variational Inference ICML 2024 Value-Based Abstraction Functions for Abstraction Sampling UAI 2024 Variance estimation in compound decision theory under boundedness NeurIPS 2024 Virtual-Event-Based Posterior Sampling and Inference for Neyman-Scott Processes JMLR 2024 When are dynamical systems learned from time series data statistically accurate? NeurIPS 2024 When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-Making AAAI 2024 Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion NeurIPS 2024