937 citations · 4k across the 76 of their papers we have counts for
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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…