26 citations · 59 across the 13 of their papers we have counts for
6 papers · 1 filter
Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions
Xiang Cheng, Bohan Wang, Jingzhao Zhang +1
MCMC algorithms offer empirically efficient tools for sampling from a target distribution . However, on the theory side, MCMC algorithms suffer from slow…
Lower Generalization Bounds for GD and SGD in Smooth Stochastic Convex Optimization
Peiyuan Zhang, Jiaye Teng, Jingzhao Zhang
This work studies the generalization error of gradient methods. More specifically, we focus on how training steps and step-size might affect generalization in smooth stocha…
Online Policy Optimization for Robust MDP
Jing Dong, Jingwei Li, Baoxiang Wang +1
Reinforcement learning (RL) has exceeded human performance in many synthetic settings such as video games and Go. However, real-world deployment of end-to-end RL models is less com…
Provably Efficient Algorithms for Multi-Objective Competitive RL
Tiancheng Yu, Yi Tian, Jingzhao Zhang +1
We study multi-objective reinforcement learning (RL) where an agent's reward is represented as a vector. In settings where an agent competes against opponents, its performance is m…
Coping with Label Shift via Distributionally Robust Optimisation
Jingzhao Zhang, Aditya Menon, Andreas Veit +3
The label shift problem refers to the supervised learning setting where the train and test label distributions do not match. Existing work addressing label shift usually assumes ac…
A Probe Towards Understanding GAN and VAE Models
Lu Mi, Macheng Shen, Jingzhao Zhang
This project report compares some known GAN and VAE models proposed prior to 2017. There has been significant progress after we finished this report. We upload this report as an in…