5 papers
Newton Method for Fixed-Support Doubly Entropic Wasserstein Barycenter
Jianting Pan, Sirong Dai, Lei Yang +3
We study the fixed-support doubly regularized Wasserstein barycenter problem. Using the semi-dual formulation of entropic optimal transport, we reformulate the problem as a smooth,…
Fast projection onto the top-k-sum constraint
Jianting Pan, Ming Yan
This paper develops an efficient algorithm for computing the Euclidean projection onto the top-k-sum constraint, a key operation in financial risk management and matrix optimizatio…
AdaGamma: State-Dependent Discounting for Temporal Adaptation in Reinforcement Learning
Yaomin Wang, Jianting Pan, Ran Tian +4
The discount factor in reinforcement learning controls both the effective planning horizon and the strength of bootstrapping, yet most deep RL methods use a single fixed value acro…
Inexact Bregman Sparse Newton Method for Efficient Optimal Transport
Jianting Pan, Ji'an Li, Ming Yan
Computing exact Optimal Transport (OT) distances for large-scale datasets is computationally prohibitive. While entropy-regularized alternatives offer speed, they sacrifice precisi…
Efficient sparse probability measures recovery via Bregman gradient
Jianting Pan, Ming Yan
This paper presents an algorithm tailored for the efficient recovery of sparse probability measures incorporating -sparse regularization within the probability simplex cons…