1 citations · 3 across the 5 of their papers we have counts for
5 papers
Byzantine-Robust Decentralized Stochastic Optimization with Stochastic Gradient Noise-Independent Learning Error
Jie Peng, Weiyu Li, Qing Ling
This paper studies Byzantine-robust stochastic optimization over a decentralized network, where every agent periodically communicates with its neighbors to exchange local models, a…
: Transferring Visual Representations for Reinforcement Learning via Prompting
Guoliang You, Xiaomeng Chu, Yifan Duan +4
It is important for deep reinforcement learning (DRL) algorithms to transfer their learned policies to new environments that have different visual inputs. In this paper, we introdu…
Deep Reinforcement Learning for Localizability-Enhanced Navigation in Dynamic Human Environments
Yuan Chen, Quecheng Qiu, Xiangyu Liu +5
Reliable localization is crucial for autonomous robots to navigate efficiently and safely. Some navigation methods can plan paths with high localizability (which describes the capa…
TLP: A Deep Learning-based Cost Model for Tensor Program Tuning
Yi Zhai, Yu Zhang, Shuo Liu +4
Tensor program tuning is a non-convex objective optimization problem, to which search-based approaches have proven to be effective. At the core of the search-based approaches lies…
Enhancing Distributional Stability among Sub-populations
Jiashuo Liu, Jiayun Wu, Jie Peng +4
Enhancing the stability of machine learning algorithms under distributional shifts is at the heart of the Out-of-Distribution (OOD) Generalization problem. Derived from causal lear…