74 citations · 171 across the 8 of their papers we have counts for
10 papers
Nonstochastic Bandits with Infinitely Many Experts
X. Flora Meng, Tuhin Sarkar, Munther A. Dahleh
We study the problem of nonstochastic bandits with expert advice, extending the setting from finitely many experts to any countably infinite set: A learner aims to maximize the tot…
Learning nonlinear dynamical systems from a single trajectory
Dylan J. Foster, Alexander Rakhlin, Tuhin Sarkar
We introduce algorithms for learning nonlinear dynamical systems of the form , where is a weight matrix, is a nonlinear link…
Data Driven Estimation of Stochastic Switched Linear Systems of Unknown Order
Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh
We address the problem of learning the parameters of a mean square stable switched linear systems (SLS) with unknown latent space dimension, or \textit{order}, from its noisy input…
Nonparametric Finite Time LTI System Identification
Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh
We address the problem of learning the parameters of a stable linear time invariant (LTI) system or linear dynamical system (LDS) with unknown latent space dimension, or order, fro…
Near optimal finite time identification of arbitrary linear dynamical systems
Tuhin Sarkar, Alexander Rakhlin
We derive finite time error bounds for estimating general linear time-invariant (LTI) systems from a single observed trajectory using the method of least squares. We provide the fi…
Minimal Realization Problems for Jump Linear Systems
Tuhin Sarkar, Mardavij Roozbehani, Munther A. Dahleh
This paper addresses two fundamental problems in the context of jump linear systems (JLS). The first problem is concerned with characterizing the minimal state space dimension sole…
A Marketplace for Data: An Algorithmic Solution
Anish Agarwal, Munther Dahleh, Tuhin Sarkar
In this work, we aim to design a data marketplace; a robust real-time matching mechanism to efficiently buy and sell training data for Machine Learning tasks. While the monetizatio…
Robustness in Consensus Networks
Tuhin Sarkar, Mardavij Roozbehani, Munther A. Dahleh
We consider the problem of robustness in large consensus networks that occur in many areas such as distributed optimization. Robustness, in this context, is the scaling of performa…
Asymptotic Network Robustness
Tuhin Sarkar, Mardavij Roozbehani, Munther A. Dahleh
This paper examines the dependence of network performance measures on network size and considers scaling results for large networks. We connect two performance measures that are we…
Lossy Compression via Sparse Linear Regression: Computationally Efficient Encoding and Decoding
Ramji Venkataramanan, Tuhin Sarkar, Sekhar Tatikonda
We propose computationally efficient encoders and decoders for lossy compression using a Sparse Regression Code. The codebook is defined by a design matrix and codewords are struct…