mixing time

A Single Stepsize Suffices for Unprojected Linear TD(0): Simultaneous Robust and Fast Rates via Polyak–Ruppert Averaging COLT 2026 Mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression JMLR 2026 Trajectory-Level Experimental Design for Fast Safety Parameter Estimation of Unknown Environments by Autonomous Systems L4DC 2026 Graph-accelerated Markov Chain Monte Carlo using Approximate Samples JMLR 2025 High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm ALT 2025 Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity JMLR 2025 Quantifying the Effectiveness of Linear Preconditioning in Markov Chain Monte Carlo JMLR 2025 Regularized Dikin Walks for Sampling Truncated Logconcave Measures, Mixed Isoperimetry and Beyond Worst-Case Analysis COLT 2025 What Makes a Good Feedforward Computational Graph? ICML 2025 Balancing Context Length and Mixing Times for Reinforcement Learning at Scale NeurIPS 2024 Conformal Predictions under Markovian Data ICML 2024 Contraction of Markovian Operators in Orlicz Spaces and Error Bounds for Markov Chain Monte Carlo (Extended Abstract) COLT 2024 Fast Equilibrium of SGD in Generic Situations ICLR 2024 Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm COLT 2024 Feasible Q-Learning for Average Reward Reinforcement Learning AISTATS 2024 Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Diffusions COLT 2024 Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence JMLR 2024 Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk NeurIPS 2024 On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling JMLR 2024 Sampling Polytopes with Riemannian HMC: Faster Mixing via the Lewis Weights Barrier COLT 2024 Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles ICML 2024 Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler COLT 2023 A Tale of Sampling and Estimation in Discounted Reinforcement Learning AISTATS 2023 Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic ICML 2023 Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators COLT 2023