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07/05/2023
Meta-Learning Adversarial Bandit Algorithms
We study online meta-learning with bandit feedback, with the goal of imp...
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04/09/2023
μ^2-SGD: Stable Stochastic Optimization via a Double Momentum Mechanism
We consider stochastic convex optimization problems where the objective ...
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04/09/2023
SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization
We consider distributed learning scenarios where M machines interact wit...
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02/01/2023
: Downlink Compression for Cross-Device Federated Learning
Many compression techniques have been proposed to reduce the communicati...
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05/31/2022
Online Meta-Learning in Adversarial Multi-Armed Bandits
We study meta-learning for adversarial multi-armed bandits. We consider ...
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02/09/2022
Adapting to Mixing Time in Stochastic Optimization with Markovian Data
We consider stochastic optimization problems where data is drawn from a ...
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02/04/2022
Robust Linear Regression for General Feature Distribution
We investigate robust linear regression where data may be contaminated b...
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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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11/01/2021
STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization
In this work we investigate stochastic non-convex optimization problems ...
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06/23/2021
Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays
We consider stochastic convex optimization problems, where several machi...
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03/23/2021
Generative Minimization Networks: Training GANs Without Competition
Many applications in machine learning can be framed as minimization prob...
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10/30/2019
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization
We propose a novel adaptive, accelerated algorithm for the stochastic co...
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10/28/2019
Adaptive Sampling for Stochastic Risk-Averse Learning
We consider the problem of training machine learning models in a risk-av...
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03/29/2019
Online Variance Reduction with Mixtures
Adaptive importance sampling for stochastic optimization is a promising ...
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02/21/2019
Multi-Player Bandits: The Adversarial Case
We consider a setting where multiple players sequentially choose among a...
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02/05/2019
A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise
We consider variational inequalities coming from monotone operators, a s...
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11/13/2018
Evaluating GANs via Duality
Generative Adversarial Networks (GANs) have shown great results in accur...
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09/08/2018
Online Adaptive Methods, Universality and Acceleration
We present a novel method for convex unconstrained optimization that, wi...
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06/19/2018
Unsupervised Imitation Learning
We introduce a novel method to learn a policy from unsupervised demonstr...
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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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02/13/2018
Online Variance Reduction for Stochastic Optimization
Modern stochastic optimization methods often rely on uniform sampling wh...
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11/04/2017
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms
DR-submodular continuous functions are important objectives with wide re...
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06/10/2017
An Online Learning Approach to Generative Adversarial Networks
We consider the problem of training generative models with a Generative ...
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05/30/2017
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes
We present an approach towards convex optimization that relies on a nove...
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01/25/2017
k*-Nearest Neighbors: From Global to Local
The weighted k-nearest neighbors algorithm is one of the most fundamenta...
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11/15/2016