88 citations · 171 across the 39 of their papers we have counts for
6 papers · 1 filter
Reinforcement Learning Using known Invariances
Alexandru Cioba, Aya Kayal, Laura Toni +2
In many real-world reinforcement learning (RL) problems, the environment exhibits inherent symmetries that can be exploited to improve learning efficiency. This paper develops a th…
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…
Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds
Aya Kayal, Sattar Vakili, Laura Toni +2
Bayesian optimization (BO) with preference-based feedback has recently garnered significant attention due to its emerging applications. We refer to this problem as Bayesian Optimiz…
Towards a Foundation Model for Communication Systems
Davide Buffelli, Sowmen Das, Yu-Wei Lin +5
Artificial Intelligence (AI) has demonstrated unprecedented performance across various domains, and its application to communication systems is an active area of research. While cu…
Group Think: Multiple Concurrent Reasoning Agents Collaborating at Token Level Granularity
Chan-Jan Hsu, Davide Buffelli, Jamie McGowan +4
Recent advances in large language models (LLMs) have demonstrated the power of reasoning through self-generated chains of thought. Multiple reasoning agents can collaborate to rais…
Near-Optimal Sample Complexity in Reward-Free Kernel-Based Reinforcement Learning
Aya Kayal, Sattar Vakili, Laura Toni +1
Reinforcement Learning (RL) problems are being considered under increasingly more complex structures. While tabular and linear models have been thoroughly explored, the analytical…