4 papers
Policy Gradient for Continuous-Time Robust Markov Decision Processes
Tanya Veeravalli, David M. Bossens, Atsushi Nitanda
The framework of robust Markov decision processes (RMDPs) allows the design of reinforcement learning agents that satisfy performance guarantees under worst-case transition dynamic…
Slowly Annealed Langevin Dynamics: Theory and Applications to Training-Free Guided Generation
Atsushi Nitanda, Dake Bu, Yueming Lyu +1
We study Slowly Annealed Langevin Dynamics (SALD), a sampler for tracking a path of moving target distributions and approximating the terminal target through time slowdown. We esta…
Statistical Analysis of the Sinkhorn Iterations for Two-Sample Schrödinger Bridge Estimation
Ibuki Maeda, Rentian Yao, Atsushi Nitanda
The Schrödinger bridge problem seeks the optimal stochastic process that connects two given probability distributions with minimal energy modification. While the Sinkhorn algorith…
Learning Density Evolution from Snapshot Data
Rentian Yao, Atsushi Nitanda, Xiaohui Chen +1
Motivated by learning dynamical structures from static snapshot data, this paper presents a distribution-on-scalar regression approach for estimating the density evolution of a sto…