160 citations · 1.5k across the 122 of their papers we have counts for
7 papers · 1 filter
Input Perturbations for Adaptive Control and Learning
Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
This paper studies adaptive algorithms for simultaneous regulation (i.e., control) and estimation (i.e., learning) of Multiple Input Multiple Output (MIMO) linear dynamical systems…
Online Multiclass Boosting with Bandit Feedback
Daniel T. Zhang, Young Hun Jung, Ambuj Tewari
We present online boosting algorithms for multiclass classification with bandit feedback, where the learner only receives feedback about the correctness of its prediction. We propo…
Fighting Contextual Bandits with Stochastic Smoothing
Young Hun Jung, Ambuj Tewari
We introduce a new stochastic smoothing perspective to study adversarial contextual bandit problems. We propose a general algorithm template that represents random perturbation bas…
On the Approximation Properties of Random ReLU Features
Yitong Sun, Anna Gilbert, Ambuj Tewari
We study the approximation properties of random ReLU features through their reproducing kernel Hilbert space (RKHS). We first prove a universality theorem for the RKHS induced by r…
But How Does It Work in Theory? Linear SVM with Random Features
Yitong Sun, Anna Gilbert, Ambuj Tewari
We prove that, under low noise assumptions, the support vector machine with random features (RFSVM) can achieve the learning rate faster than on a training…
Finite Time Adaptive Stabilization of LQ Systems
Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
Stabilization of linear systems with unknown dynamics is a canonical problem in adaptive control. Since the lack of knowledge of system parameters can cause it to become destabiliz…