Probability

Gambling-Based Confidence Sequences for Bounded Random Vectors ICML 2024 Generative Fractional Diffusion Models NeurIPS 2024 Graph fission and cross-validation AISTATS 2024 Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series NeurIPS 2024 Hawkes-Enhanced Spatial-Temporal Hypergraph Contrastive Learning Based on Criminal Correlations AAAI 2024 Hidden Population Estimation with Indirect Inference and Auxiliary Information UAI 2024 High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails ICML 2024 Hodge-Compositional Edge Gaussian Processes AISTATS 2024 Identifiability Analysis of Linear ODE Systems with Hidden Confounders NeurIPS 2024 Is Score Matching Suitable for Estimating Point Processes? NeurIPS 2024 Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting ICLR 2024 Kernel Thinning JMLR 2024 Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited ICLR 2024 Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning NeurIPS 2024 Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck ICLR 2024 Learning Distributions on Manifolds with Free-Form Flows NeurIPS 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