10 citations · 31 across the 13 of their papers we have counts for
14 papers
Replicable Dueling Bandits
Rohan Deb, Aadirupa Saha, Udaya Ghai +2
Dueling bandits choose each comparison based on earlier noisy outcomes, so two runs on the same preferences can compare different pairs. We study replicable dueling bandits, where…
Replicable Bandits with UCB based Exploration
Rohan Deb, Udaya Ghai, Karan Singh +1
We study replicable algorithms for stochastic multi-armed bandits (MAB) and linear bandits with UCB (Upper Confidence Bound) based exploration. A bandit algorithm is -replicable…
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…
Sample-Efficient Agnostic Boosting
Udaya Ghai, Karan Singh
The theory of boosting provides a computational framework for aggregating approximate weak learning algorithms, which perform marginally better than a random predictor, into an acc…
Online Nonstochastic Model-Free Reinforcement Learning
Udaya Ghai, Arushi Gupta, Wenhan Xia +2
We investigate robust model-free reinforcement learning algorithms designed for environments that may be dynamic or even adversarial. Traditional state-based policies often struggl…
Non-convex online learning via algorithmic equivalence
Udaya Ghai, Zhou Lu, Elad Hazan
We study an algorithmic equivalence technique between non-convex gradient descent and convex mirror descent. We start by looking at a harder problem of regret minimization in onlin…
A Regret Minimization Approach to Multi-Agent Control
Udaya Ghai, Udari Madhushani, Naomi Leonard +1
We study the problem of multi-agent control of a dynamical system with known dynamics and adversarial disturbances. Our study focuses on optimal control without centralized precomp…
Machine Learning for Mechanical Ventilation Control (Extended Abstract)
Daniel Suo, Naman Agarwal, Wenhan Xia +12
Mechanical ventilation is one of the most widely used therapies in the ICU. However, despite broad application from anaesthesia to COVID-related life support, many injurious challe…
Robust Online Control with Model Misspecification
Xinyi Chen, Udaya Ghai, Elad Hazan +1
We study online control of an unknown nonlinear dynamical system that is approximated by a time-invariant linear system with model misspecification. Our study focuses on robustness…
Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking
Paula Gradu, John Hallman, Daniel Suo +7
We present an open-source library of natively differentiable physics and robotics environments, accompanied by gradient-based control methods and a benchmark-ing suite. The introdu…
Machine Learning for Mechanical Ventilation Control
Daniel Suo, Naman Agarwal, Wenhan Xia +12
We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs acco…
Generating Adversarial Disturbances for Controller Verification
Udaya Ghai, David Snyder, Anirudha Majumdar +1
We consider the problem of generating maximally adversarial disturbances for a given controller assuming only blackbox access to it. We propose an online learning approach to this…
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…
Exponentiated Gradient Meets Gradient Descent
Udaya Ghai, Elad Hazan, Yoram Singer
The (stochastic) gradient descent and the multiplicative update method are probably the most popular algorithms in machine learning. We introduce and study a new regularization whi…