activity
20172022
most citedKernel Methods for Cooperative Multi-Agent Contextual Bandits

7 citations · 24 across the 7 of their papers we have counts for

collaborators

20 papers

stat.ML2022

Private and Byzantine-Proof Cooperative Decision-Making

Abhimanyu Dubey, Alex Pentland

The cooperative bandit problem is a multi-agent decision problem involving a group of agents that interact simultaneously with a multi-armed bandit, while communicating over a netw…

cs.HC2021

Social influence leads to the formation of diverse local trends

Ziv Epstein, Matthew Groh, Abhimanyu Dubey +1

How does the visual design of digital platforms impact user behavior and the resulting environment? A body of work suggests that introducing social signals to content can increase…

cs.CV2021

Adaptive Methods for Real-World Domain Generalization

Abhimanyu Dubey, Vignesh Ramanathan, Alex Pentland +1

Invariant approaches have been remarkably successful in tackling the problem of domain generalization, where the objective is to perform inference on data distributions different f…

cs.LG20214 cited

Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation

Abhimanyu Dubey, Alex Pentland

Reinforcement learning in cooperative multi-agent settings has recently advanced significantly in its scope, with applications in cooperative estimation for advertising, dynamic tr…

stat.ML20212 cited

No-Regret Algorithms for Private Gaussian Process Bandit Optimization

Abhimanyu Dubey

The widespread proliferation of data-driven decision-making has ushered in a recent interest in the design of privacy-preserving algorithms. In this paper, we consider the ubiquito…

cs.LG2020

Differentially-Private Federated Linear Bandits

Abhimanyu Dubey, Alex Pentland

The rapid proliferation of decentralized learning systems mandates the need for differentially-private cooperative learning. In this paper, we study this in context of the contextu…