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Found 5 papers, 1 papers with code
Robust Multi-Agent Decision-Making with Heavy-Tailed Payoffs
We study the heavy-tailed stochastic bandit problem in the cooperative multiagent setting, where a group of agents interact with a common bandit problem, while communicating on a network with delays.
Kernel Methods for Cooperative Multi-Agent Learning with Delays
We propose Coop-KernelUCB that provides near-optimal bounds on the per-agent regret in this setting, and is both computationally and communicatively efficient.
LePaRD: A Large-Scale Dataset of Judges Citing Precedents
We present the Legal Passage Retrieval Dataset LePaRD.
Understanding Human Judgments of Causality
We also analyzed the differences between the expert and non-expert machine algorithms based on their neural representations to evaluate the performances, providing insight into the human experts' and non-experts' cognitive abilities.
How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology
This is an important problem because a common technique to improve speed and robustness of learning in deep reinforcement learning and many other machine learning algorithms is to run multiple learning agents in parallel.
