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cs.LG2025
HiPPO-Prophecy: State-Space Models can Provably Learn Dynamical Systems in Context
Federico Arangath Joseph, Kilian Konstantin Haefeli, Noah Liniger +1
This work explores the in-context learning capabilities of State Space Models (SSMs) and presents, to the best of our knowledge, the first theoretical explanation of a possible und…
cs.LG2024
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
Riccardo De Santi, Federico Arangath Joseph, Noah Liniger +2
How can a scientist use a Reinforcement Learning (RL) algorithm to design experiments over a dynamical system's state space? In the case of finite and Markovian systems, an area ca…