3 papers
math.ST2026
Accelerating Langevin Monte Carlo via Efficient Stochastic Runge--Kutta Methods beyond Log-Concavity
Bin Yang, Xiaojie Wang
Sampling from a high-dimensional probability distribution is a fundamental algorithmic task arising in wide-ranging applications across multiple disciplines, including scientific c…
stat.ML2026
When Langevin Monte Carlo Meets Randomization: New Sampling Algorithms with Non-asymptotic Error Bounds beyond Log-Concavity and Gradient Lipschitzness
Xiaojie Wang, Bin Yang
Efficient sampling from complex and high dimensional target distributions turns out to be a fundamental task in diverse disciplines such as scientific computing, statistics and mac…
cs.RO2026
Generalizable Dense Reward for Long-Horizon Robotic Tasks
Silong Yong, Stephen Sheng, Carl Qi +6
Existing robotic foundation policies are trained primarily via large-scale imitation learning. While such models demonstrate strong capabilities, they often struggle with long-hori…