8 papers
Wireless Physical-Layer Foundation Models: Architectures, Learning Paradigms, Applications, and Deployment
Mohammad Cheraghinia, Davide Buffelli, Liu Li +6
Foundation models, i.e., large neural networks pretrained on broad unlabeled data and adapted to many downstream tasks, have reshaped natural language processing and computer visio…
Learning Kernel-Based MDPs from Episodic Preferential Feedback
Nikola Pavlovic, Sattar Vakili, Qing Zhao
Human feedback often arrives as preferences rather than calibrated numeric rewards, motivating reinforcement learning from preferential feedback, also referred to as reinforcement…
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…
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…
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…