11 papers
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…
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…
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…
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…