18 citations · 20 across the 23 of their papers we have counts for
14 papers · 1 filter
Two-Layer Linear Auto-Regressive Models Estimate Latent States
Yahya Sattar, Sunmook Choi, Leo Maynard-Zhang +3
Auto-regressive models have emerged as powerful tools for sequential data, from language to video. Understanding how and why these models learn latent representations remains an op…
Instance-Optimal Estimation with Multiple LLM Judges on a Budget
Junghyun Lee, Sanghwa Kim, Yassir Jedra +2
Evaluating large language models increasingly relies on LLM-as-a-judge protocols, but such evaluations remain costly: different judges have different prices and reliabilities, and…
Explore-then-Commit for Nonstationary Linear Bandits with Latent Dynamics
Sunmook Choi, Yahya Sattar, Yassir Jedra +2
We study a nonstationary bandit problem where rewards depend on both actions and latent states, the latter governed by unknown linear dynamics. Crucially, the state dynamics also d…
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion
Akhil Jalan, Yassir Jedra, Arya Mazumdar +2
We study transfer learning for matrix completion in a Missing Not-at-Random (MNAR) setting that is motivated by biological problems. The target matrix has entire rows and colum…
-SVD with Gradient Descent
Yassir Jedra, Devavrat Shah
The emergence of modern compute infrastructure for iterative optimization has led to great interest in developing optimization-based approaches for a scalable computation of -SV…
Finite Sample Identification of Partially Observed Bilinear Dynamical Systems
Yahya Sattar, Yassir Jedra, Maryam Fazel +1
We consider the problem of learning a realization of a partially observed bilinear dynamical system (BLDS) from noisy input-output data. Given a single trajectory of input-output s…
Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation
Stefan Stojanovic, Yassir Jedra, Alexandre Proutiere
We consider the problem of learning an -optimal policy in controlled dynamical systems with low-rank latent structure. For this problem, we present LoRa-PI (Low-Rank P…
Learning Linear Dynamics from Bilinear Observations
Yahya Sattar, Yassir Jedra, Sarah Dean
We consider the problem of learning a realization of a partially observed dynamical system with linear state transitions and bilinear observations. Under very mild assumptions on t…
Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery
Yassir Jedra, William Réveillard, Stefan Stojanovic +1
We study contextual bandits with low-rank structure where, in each round, if the (context, arm) pair is selected, the learner observes a noisy sample of th…
Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning
Stefan Stojanovic, Yassir Jedra, Alexandre Proutiere
We study matrix estimation problems arising in reinforcement learning (RL) with low-rank structure. In low-rank bandits, the matrix to be recovered specifies the expected arm rewar…
Best Policy Identification in Linear MDPs
Jerome Taupin, Yassir Jedra, Alexandre Proutiere
We investigate the problem of best policy identification in discounted linear Markov Decision Processes in the fixed confidence setting under a generative model. We first derive an…
Nearly Optimal Latent State Decoding in Block MDPs
Yassir Jedra, Junghyun Lee, Alexandre Proutière +1
We investigate the problems of model estimation and reward-free learning in episodic Block MDPs. In these MDPs, the decision maker has access to rich observations or contexts gener…
Learning Optimal Antenna Tilt Control Policies: A Contextual Linear Bandit Approach
Filippo Vannella, Alexandre Proutiere, Yassir Jedra +1
Controlling antenna tilts in cellular networks is imperative to reach an efficient trade-off between network coverage and capacity. In this paper, we devise algorithms learning opt…
Minimal Expected Regret in Linear Quadratic Control
Yassir Jedra, Alexandre Proutiere
We consider the problem of online learning in Linear Quadratic Control systems whose state transition and state-action transition matrices and may be initially unknown. We…