5 papers · 1 filter
Minimizing Human Intervention in Online Classification
William Réveillard, Vasileios Saketos, Alexandre Proutiere +1
Training or fine-tuning large language model (LLM)-based systems often requires costly human feedback, yet there is limited understanding of how to minimize such intervention while…
Near-optimal Rank Adaptive Inference of High Dimensional Matrices
Frédéric Zheng, Yassir Jedra, Alexandre Proutiere
We address the problem of estimating a high-dimensional matrix from linear measurements, with a focus on designing optimal rank-adaptive algorithms. These algorithms infer the matr…
Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning
Bastien Dubail, Stefan Stojanovic, Alexandre Proutière
Low-rank structure is a common implicit assumption in many modern reinforcement learning (RL) algorithms. For instance, reward-free and goal-conditioned RL methods often presume th…
Minimal Order Recovery through Rank-adaptive Identification
Frédéric Zheng, Yassir Jedra, Alexandre Proutière
This paper addresses the problem of identifying linear systems from noisy input-output trajectories. We introduce Thresholded Ho-Kalman, an algorithm that leverages a rank-adaptive…
Near-Optimal Clustering in Mixture of Markov Chains
Junghyun Lee, Yassir Jedra, Alexandre Proutière +1
We study the problem of clustering trajectories of length , each generated by one of K unknown ergodic Markov chains over a finite state space of size . We derive an inst…