10 citations · 17 across the 9 of their papers we have counts for
6 papers · 1 filter
Generalized Intention Modeling in Multi-Agent Reinforcement Learning
Mateusz Odrowaz-Sypniewski, Jasmine Bayrooti, Ajay Shankar +1
Modeling an opponent's intent is critical for effective decision-making in non-cooperative, competitive, and general-sum multi-agent reinforcement learning. Existing opponent model…
Learning Mixture Density via Natural Gradient Expectation Maximization
Yutao Chen, Jasmine Bayrooti, Steven Morad
Mixture density networks are neural networks that produce Gaussian mixtures to represent continuous multimodal conditional densities. Standard training procedures involve maximum l…
No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes
Jasmine Bayrooti, Sattar Vakili, Amanda Prorok +1
Thompson sampling (TS) is a powerful and widely used strategy for sequential decision-making, with applications ranging from Bayesian optimization to reinforcement learning (RL). D…
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
Jasmine Bayrooti, Carl Henrik Ek, Amanda Prorok
Learning complex robot behavior through interactions with the environment necessitates principled exploration. Effective strategies should prioritize exploring regions of the state…
Generalizing Differentially Private Decentralized Deep Learning with Multi-Agent Consensus
Jasmine Bayrooti, Zhan Gao, Amanda Prorok
Cooperative decentralized learning relies on direct information exchange between communicating agents, each with access to locally available datasets. The goal is to agree on model…
Temperature as Uncertainty in Contrastive Learning
Oliver Zhang, Mike Wu, Jasmine Bayrooti +1
Contrastive learning has demonstrated great capability to learn representations without annotations, even outperforming supervised baselines. However, it still lacks important prop…