Stochastic Processes

Learning Mixtures of Markov Chains and MDPs ICML 2023 Limits of Dense Simplicial Complexes JMLR 2023 Local Dependence Graphs for Discrete Time Processes CLeaR 2023 Neural Markov Jump Processes ICML 2023 Nonparametric Density Estimation under Distribution Drift ICML 2023 Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds NeurIPS 2023 Randomized geometric tools for anomaly detection in stock markets AISTATS 2023 Reducing the Prior Mismatch of Stochastic Differential Equations for Diffusion-based Speech Enhancement Interspeech 2023 Sampling From a Schrödinger Bridge AISTATS 2023 Sampling random graph homomorphisms and applications to network data analysis JMLR 2023 Separate but Equal: Equality in Belief Propagation for Single Cycle Graphs AAAI 2023 Statistical Comparisons of Classifiers by Generalized Stochastic Dominance JMLR 2023 The k-Cap Process on Geometric Random Graphs COLT 2023 The Implicit Regularization of Dynamical Stability in Stochastic Gradient Descent ICML 2023 Toward L_∞Recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields COLT 2023 Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality COLT 2023 Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics ICLR 2023 Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms COLT 2023 Variational Autoencoding Neural Operators ICML 2023 Vector-Valued Control Variates ICML 2023 Weak Recovery Threshold for the Hypergraph Stochastic Block Model COLT 2023 Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals AISTATS 2022 Adaptive Data Analysis with Correlated Observations ICML 2022 Adaptive Importance Sampling meets Mirror Descent : a Bias-variance Tradeoff AISTATS 2022 A Langevin-like Sampler for Discrete Distributions ICML 2022