5 citations · 13 across the 4 of their papers we have counts for
7 papers
Byzantine-Robust Federated Linear Bandits
Ali Jadbabaie, Haochuan Li, Jian Qian +1
In this paper, we study a linear bandit optimization problem in a federated setting where a large collection of distributed agents collaboratively learn a common linear bandit mode…
Robust learning under clean-label attack
Avrim Blum, Steve Hanneke, Jian Qian +1
We study the problem of robust learning under clean-label data-poisoning attacks, where the attacker injects (an arbitrary set of) correctly-labeled examples to the training set to…
Stochastic Bandits with Vector Losses: Minimizing -Norm of Relative Losses
Xuedong Shang, Han Shao, Jian Qian
Multi-armed bandits are widely applied in scenarios like recommender systems, for which the goal is to maximize the click rate. However, more factors should be considered, e.g., us…
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
Yi Tian, Jian Qian, Suvrit Sra
We study minimax optimal reinforcement learning in episodic factored Markov decision processes (FMDPs), which are MDPs with conditionally independent transition components. Assumin…
Concentration Inequalities for Multinoulli Random Variables
Jian Qian, Ronan Fruit, Matteo Pirotta +1
We investigate concentration inequalities for Dirichlet and Multinomial random variables.
Importance Resampling for Off-policy Prediction
Matthew Schlegel, Wesley Chung, Daniel Graves +2
Importance sampling (IS) is a common reweighting strategy for off-policy prediction in reinforcement learning. While it is consistent and unbiased, it can result in high variance u…