235 citations · 901 across the 97 of their papers we have counts for
107 papers
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning
Pier Giuseppe Sessa, Pierre Laforgue, Nicolò Cesa-Bianchi +1
Multitask learning is a powerful framework that enables one to simultaneously learn multiple related tasks by sharing information between them. Quantifying uncertainty in the estim…
Submodular Reinforcement Learning
Manish Prajapat, Mojmír Mutný, Melanie N. Zeilinger +1
In reinforcement learning (RL), rewards of states are typically considered additive, and following the Markov assumption, they are of states visited previous…
Anytime Model Selection in Linear Bandits
Parnian Kassraie, Nicolas Emmenegger, Andreas Krause +1
Model selection in the context of bandit optimization is a challenging problem, as it requires balancing exploration and exploitation not only for action selection, but also for mo…
Safe Risk-averse Bayesian Optimization for Controller Tuning
Christopher Koenig, Miks Ozols, Anastasia Makarova +3
Controller tuning and parameter optimization are crucial in system design to improve both the controller and underlying system performance. Bayesian optimization has been establish…
Provably Learning Nash Policies in Constrained Markov Potential Games
Pragnya Alatur, Giorgia Ramponi, Niao He +1
Multi-agent reinforcement learning (MARL) addresses sequential decision-making problems with multiple agents, where each agent optimizes its own objective. In many real-world insta…
Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning
Matej Jusup, Barna Pásztor, Tadeusz Janik +4
Many applications, e.g., in shared mobility, require coordinating a large number of agents. Mean-field reinforcement learning addresses the resulting scalability challenge by optim…