96 citations · 135 across the 9 of their papers we have counts for
12 papers · 1 filter
Sample-Optimal Agnostic Boosting with Unlabeled Data
Udaya Ghai, Karan Singh
Boosting provides a practical and provably effective framework for constructing accurate learning algorithms from inaccurate rules of thumb. It extends the promise of sample-effici…
Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret
Gautam Goel, Naman Agarwal, Karan Singh +1
We consider the fundamental problem of online control of a linear dynamical system from two different viewpoints: regret minimization and competitive analysis. We prove that the op…
A Regret Minimization Approach to Iterative Learning Control
Naman Agarwal, Elad Hazan, Anirudha Majumdar +1
We consider the setting of iterative learning control, or model-based policy learning in the presence of uncertain, time-varying dynamics. In this setting, we propose a new perform…
Boosting for Online Convex Optimization
Elad Hazan, Karan Singh
We consider the decision-making framework of online convex optimization with a very large number of experts. This setting is ubiquitous in contextual and reinforcement learning pro…
No-Regret Prediction in Marginally Stable Systems
Udaya Ghai, Holden Lee, Karan Singh +2
We consider the problem of online prediction in a marginally stable linear dynamical system subject to bounded adversarial or (non-isotropic) stochastic perturbations. This poses t…
Improper Learning for Non-Stochastic Control
Max Simchowitz, Karan Singh, Elad Hazan
We consider the problem of controlling a possibly unknown linear dynamical system with adversarial perturbations, adversarially chosen convex loss functions, and partially observed…