89 citations · 302 across the 30 of their papers we have counts for
4 papers · 1 filter
Federated Online and Bandit Convex Optimization
Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha +1
We study the problems of distributed online and bandit convex optimization against an adaptive adversary. We aim to minimize the average regret on machines working in parallel…
On the Effect of Defections in Federated Learning and How to Prevent Them
Minbiao Han, Kumar Kshitij Patel, Han Shao +1
Federated learning is a machine learning protocol that enables a large population of agents to collaborate over multiple rounds to produce a single consensus model. There are sever…
Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective
Haoran He, Peilin Wu, Chenjia Bai +5
Reinforcement Learning (RL) has recently achieved remarkable success in robotic control. However, most works in RL operate in simulated environments where privileged knowledge (e.g…
Exploration in Model-based Reinforcement Learning with Randomized Reward
Lingxiao Wang, Ping Li
Model-based Reinforcement Learning (MBRL) has been widely adapted due to its sample efficiency. However, existing worst-case regret analysis typically requires optimistic planning,…