9 citations · 34 across the 10 of their papers we have counts for
11 papers
A Unified Kalman-Type Classification of Controllability for Linear Stochastic Systems
Qi Lü, Yu Wang
We provide a unified algebraic classification of approximate controllability, approximate null controllability, exact null controllability, and exact controllability for finite-dim…
Convex and Non-convex Optimization Under Generalized Smoothness
Haochuan Li, Jian Qian, Yi Tian +2
Classical analysis of convex and non-convex optimization methods often requires the Lipshitzness of the gradient, which limits the analysis to functions bounded by quadratics. Rece…
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
Dylan J. Foster, Noah Golowich, Jian Qian +2
We consider the problem of interactive decision making, encompassing structured bandits and reinforcement learning with general function approximation. Recently, Foster et al. (202…
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…
The Statistical Complexity of Interactive Decision Making
Dylan J. Foster, Sham M. Kakade, Jian Qian +1
A fundamental challenge in interactive learning and decision making, ranging from bandit problems to reinforcement learning, is to provide sample-efficient, adaptive learning algor…
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…
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes
Jian Qian, Ronan Fruit, Matteo Pirotta +1
We introduce and analyse two algorithms for exploration-exploitation in discrete and continuous Markov Decision Processes (MDPs) based on exploration bonuses. SCAL is a variant…