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20222025
most citedByzantine-Resilient Decentralized Multi-Armed Bandits

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2025

Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation

Yongye Su, Zeya Zhang, Jane Kou +5

Beyond general web-scale search, social network search uniquely enables users to retrieve information and discover potential connections within their social context. We introduce a…

cs.IR2024

Epinet for Content Cold Start

Hong Jun Jeon, Songbin Liu, Yuantong Li +5

The exploding popularity of online content and its user base poses an evermore challenging matching problem for modern recommendation systems. Unlike other frontiers of machine lea…

cs.LG2024

Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems

Wesley A. Suttle, Vipul K. Sharma, Krishna C. Kosaraju +4

We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of co…

cs.LG2023★ 1 cited

Byzantine-Resilient Decentralized Multi-Armed Bandits

Jingxuan Zhu, Alec Koppel, Alvaro Velasquez +1

In decentralized cooperative multi-armed bandits (MAB), each agent observes a distinct stream of rewards, and seeks to exchange information with others to select a sequence of arms…

cs.LG2022

Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search

Wesley A. Suttle, Alec Koppel, Ji Liu

In this work, we propose an information-directed objective for infinite-horizon reinforcement learning (RL), called the occupancy information ratio (OIR), inspired by the informati…