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07/15/2023
On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms
Efficient learning in multi-armed bandit mechanisms such as pay-per-clic...
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06/20/2023
Randomized Quantization is All You Need for Differential Privacy in Federated Learning
Federated learning (FL) is a common and practical framework for learning...
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05/30/2023
On Riemannian Projection-free Online Learning
The projection operation is a critical component in a wide range of opti...
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05/27/2023
Faster Margin Maximization Rates for Generic Optimization Methods
First-order optimization methods tend to inherently favor certain soluti...
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05/26/2023
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks
We study the phenomenon of in-context learning (ICL) exhibited by large ...
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02/17/2023
Minimizing Dynamic Regret on Geodesic Metric Spaces
In this paper, we consider the sequential decision problem where the goa...
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10/17/2022
Adaptive Oracle-Efficient Online Learning
The classical algorithms for online learning and decision-making have th...
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10/17/2022
On Accelerated Perceptrons and Beyond
The classical Perceptron algorithm of Rosenblatt can be used to find a l...
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11/22/2021
No-Regret Dynamics in the Fenchel Game: A Unified Framework for Algorithmic Convex Optimization
We develop an algorithmic framework for solving convex optimization prob...
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06/05/2021
Escaping Saddle Points Faster with Stochastic Momentum
Stochastic gradient descent (SGD) with stochastic momentum is popular in...
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03/01/2021
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness
Alongside the well-publicized accomplishments of deep neural networks th...
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11/17/2020
Linear Separation via Optimism
Binary linear classification has been explored since the very early days...
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10/04/2020
Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron
Over-parametrization has become a popular technique in deep learning. It...
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10/04/2020
Provable Acceleration of Neural Net Training via Polyak's Momentum
Incorporating a so-called "momentum" dynamic in gradient descent methods...
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10/04/2020
Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization
The Heavy Ball Method, proposed by Polyak over five decades ago, is a fi...
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06/19/2020
Online Kernel based Generative Adversarial Networks
One of the major breakthroughs in deep learning over the past five years...
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06/11/2020
Adaptive Sampling to Reduce Disparate Performance
Existing methods for reducing disparate performance of a classifier acro...
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11/19/2019
Fictitious Play: Convergence, Smoothness, and Optimism
We consider the dynamics of two-player zero-sum games, with the goal of ...
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07/17/2019
Competing Against Equilibria in Zero-Sum Games with Evolving Payoffs
We study the problem of repeated play in a zero-sum game in which the pa...
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06/05/2019
Last-iterate convergence rates for min-max optimization
We study the problem of finding min-max solutions for smooth two-input o...
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07/27/2018
Acceleration through Optimistic No-Regret Dynamics
We consider the problem of minimizing a smooth convex function by reduci...
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06/10/2018
ActiveRemediation: The Search for Lead Pipes in Flint, Michigan
We detail our ongoing work in Flint, Michigan to detect pipes made of le...
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05/17/2018
Faster Rates for Convex-Concave Games
We consider the use of no-regret algorithms to compute equilibria for pa...
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07/05/2017
A Data Science Approach to Understanding Residential Water Contamination in Flint
When the residents of Flint learned that lead had contaminated their wat...
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05/19/2017
On Convergence and Stability of GANs
We propose studying GAN training dynamics as regret minimization, which ...
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12/14/2015
Fighting Bandits with a New Kind of Smoothness
We define a novel family of algorithms for the adversarial multi-armed b...
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02/20/2015
Low-Cost Learning via Active Data Procurement
We design mechanisms for online procurement of data held by strategic ag...
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02/22/2014