Stochastic Processes

Adaptive Annealed Importance Sampling with Constant Rate Progress ICML 2023 Adaptive Tuning for Metropolis Adjusted Langevin Trajectories AISTATS 2023 A Fast Algorithm for Computing Prefix Probabilities ACL 2023 A Faster Sampler for Discrete Determinantal Point Processes AISTATS 2023 Aligned Diffusion Schrödinger Bridges UAI 2023 A Measure-Theoretic Characterization of Tight Language Models ACL 2023 A Tale of Sampling and Estimation in Discounted Reinforcement Learning AISTATS 2023 Concentration analysis of multivariate elliptic diffusions JMLR 2023 Contextual Stochastic Block Model: Sharp Thresholds and Contiguity JMLR 2023 Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations AAAI 2023 Differentially Private Hypothesis Testing for Linear Regression JMLR 2023 Discrete Langevin Samplers via Wasserstein Gradient Flow AISTATS 2023 Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction ICML 2023 Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence JMLR 2023 Efficient Informed Proposals for Discrete Distributions via Newton’s Series Approximation AISTATS 2023 Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models NeurIPS 2023 Fast Computation of Branching Process Transition Probabilities via ADMM AISTATS 2023 FLIP: A Utility Preserving Privacy Mechanism for Time Series JMLR 2023 Gaussian processes at the Helm(holtz): A more fluid model for ocean currents ICML 2023 Gibbsian Polar Slice Sampling ICML 2023 Huber-robust confidence sequences AISTATS 2023 Image Restoration with Mean-Reverting Stochastic Differential Equations ICML 2023 Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations JMLR 2023 Initialization Noise in Image Gradients and Saliency Maps CVPR 2023 Instrumental Processes Using Integrated Covariances CLeaR 2023