Stochastic Processes

Kernel Thinning JMLR 2024 Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited ICLR 2024 Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck ICLR 2024 Learning from many trajectories JMLR 2024 Learning Macroscopic Dynamics from Partial Microscopic Observations NeurIPS 2024 Learning multivariate temporal point processes via the time-change theorem AISTATS 2024 Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds ICML 2024 Learning the Infinitesimal Generator of Stochastic Diffusion Processes NeurIPS 2024 Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk NeurIPS 2024 Low Degree Hardness for Broadcasting on Trees NeurIPS 2024 Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods NeurIPS 2024 Mean-Field Langevin Dynamics for Signed Measures via a Bilevel Approach NeurIPS 2024 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 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