stochastic process

Neural Sampling in Hierarchical Exponential-family Energy-based Models NeurIPS 2023 Noise-Adaptive Thompson Sampling for Linear Contextual Bandits NeurIPS 2023 Nonparametric Density Estimation under Distribution Drift ICML 2023 ODE-RSSM: Learning Stochastic Recurrent State Space Model from Irregularly Sampled Data AAAI 2023 Online Reinforcement Learning with Uncertain Episode Lengths AAAI 2023 On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions NeurIPS 2023 On the Role of Memory in Robust Opinion Dynamics IJCAI 2023 On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences NeurIPS 2023 Probabilistic Programs as an Action Description Language AAAI 2023 Robust Load Balancing with Machine Learned Advice JMLR 2023 Sampling From a Schrödinger Bridge AISTATS 2023 Scalable marked point processes for exchangeable and non-exchangeable event sequences AISTATS 2023 Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process ICCV 2023 SGD with Large Step Sizes Learns Sparse Features ICML 2023 Smooth Non-stationary Bandits ICML 2023 Spectral Decomposition Representation for Reinforcement Learning ICLR 2023 Spotlight News Driven Quantitative Trading Based on Trajectory Optimization IJCAI 2023 Stochastic Generative Flow Networks UAI 2023 STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning NeurIPS 2023 Streaming PCA for Markovian Data NeurIPS 2023 TD Convergence: An Optimization Perspective NeurIPS 2023 The k-Cap Process on Geometric Random Graphs COLT 2023 The Behavior and Convergence of Local Bayesian Optimization NeurIPS 2023 Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality COLT 2023 Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach NeurIPS 2023