937 citations · 4k across the 76 of their papers we have counts for
78 papers
Stochastic Gradient Succeeds for Bandits
Jincheng Mei, Zixin Zhong, Bo Dai +3
We show that the \emph{stochastic gradient} bandit algorithm converges to a \emph{globally optimal} policy at an rate, even with a \emph{constant} step size. Remarkably, g…
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks
Jacob Abernethy, Alekh Agarwal, Teodor V. Marinov +1
We study the phenomenon of \textit{in-context learning} (ICL) exhibited by large language models, where they can adapt to a new learning task, given a handful of labeled examples,…
An Empirical Evaluation of Federated Contextual Bandit Algorithms
Alekh Agarwal, H. Brendan McMahan, Zheng Xu
As the adoption of federated learning increases for learning from sensitive data local to user devices, it is natural to ask if the learning can be done using implicit signals gene…
Leveraging User-Triggered Supervision in Contextual Bandits
Alekh Agarwal, Claudio Gentile, Teodor V. Marinov
We study contextual bandit (CB) problems, where the user can sometimes respond with the best action in a given context. Such an interaction arises, for example, in text prediction…
Learning in POMDPs is Sample-Efficient with Hindsight Observability
Jonathan N. Lee, Alekh Agarwal, Christoph Dann +1
POMDPs capture a broad class of decision making problems, but hardness results suggest that learning is intractable even in simple settings due to the inherent partial observabilit…
VOL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation
Alekh Agarwal, Yujia Jin, Tong Zhang
We study time-inhomogeneous episodic reinforcement learning (RL) under general function approximation and sparse rewards. We design a new algorithm, Variance-weighted Optimistic $Q…