18 citations · 20 across the 23 of their papers we have counts for
24 papers
Learning and Control Beyond Linearity: Towards a Non-asymptotic Theory for Bilinear Systems
Yahya Sattar, Yassir Jedra, Robin Strässer +3
This tutorial provides a unified view of the emerging area of bilinear learning and control. Using linear systems as a benchmark, it explains what fundamentally changes in the bili…
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…
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…
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…
Sub-optimality of the Separation Principle for Quadratic Control from Bilinear Observations
Yahya Sattar, Sunmook Choi, Yassir Jedra +2
We consider the problem of controlling a linear dynamical system from bilinear observations with minimal quadratic cost. Despite the similarity of this problem to standard linear q…
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…
Constrained Deep Reinforcement Learning for Fronthaul Compression Optimization
Axel Grönland, Alessio Russo, Yassir Jedra +2
In the Centralized-Radio Access Network (C-RAN) architecture, functions can be placed in the central or distributed locations. This architecture can offer higher capacity and cost…
A Tutorial on the Non-Asymptotic Theory of System Identification
Ingvar Ziemann, Anastasios Tsiamis, Bruce Lee +3
This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification. We emphasize tools we deem particul…
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…
Optimal Best-arm Identification in Linear Bandits
Yassir Jedra, Alexandre Proutiere
We study the problem of best-arm identification with fixed confidence in stochastic linear bandits. The objective is to identify the best arm with a given level of certainty while…
Finite-time Identification of Stable Linear Systems: Optimality of the Least-Squares Estimator
Yassir Jedra, Alexandre Proutiere
We present a new finite-time analysis of the estimation error of the Ordinary Least Squares (OLS) estimator for stable linear time-invariant systems. We characterize the number of…
Sample Complexity Lower Bounds for Linear System Identification
Yassir Jedra, Alexandre Proutiere
This paper establishes problem-specific sample complexity lower bounds for linear system identification problems. The sample complexity is defined in the PAC framework: it correspo…