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Stochastic Processes

373 directly classified papers

Papers per year

Papers

Variational Autoencoding Neural Operators ICML 2023
Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction ICML 2023
Image Restoration with Mean-Reverting Stochastic Differential Equations ICML 2023
Neural Markov Jump Processes ICML 2023
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds NIPS 2023
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents ICML 2023
Adaptive Annealed Importance Sampling with Constant Rate Progress ICML 2023
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance JMLR 2023
A Measure-Theoretic Characterization of Tight Language Models ACL 2023
Limits of Dense Simplicial Complexes JMLR 2023
Differentially Private Hypothesis Testing for Linear Regression JMLR 2023
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence JMLR 2023
Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations JMLR 2023
Learning Mixtures of Markov Chains and MDPs ICML 2023
Sampling random graph homomorphisms and applications to network data analysis JMLR 2023
The $k$-Cap Process on Geometric Random Graphs COLT 2023
Adaptive Tuning for Metropolis Adjusted Langevin Trajectories AISTATS 2023
Efficient Informed Proposals for Discrete Distributions via Newton’s Series Approximation AISTATS 2023
Discrete Langevin Samplers via Wasserstein Gradient Flow AISTATS 2023
A Faster Sampler for Discrete Determinantal Point Processes AISTATS 2023
Contextual Stochastic Block Model: Sharp Thresholds and Contiguity JMLR 2023
Sampling From a Schrödinger Bridge AISTATS 2023
Huber-robust confidence sequences AISTATS 2023
Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality COLT 2023
A Tale of Sampling and Estimation in Discounted Reinforcement Learning AISTATS 2023