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06/08/2023
Unconstrained Online Learning with Unbounded Losses
Algorithms for online learning typically require one or more boundedness...
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01/31/2023
Unconstrained Dynamic Regret via Sparse Coding
Motivated by time series forecasting, we study Online Linear Optimizatio...
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11/24/2022
Differentially Private Image Classification from Features
Leveraging transfer learning has recently been shown to be an effective ...
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10/25/2022
Parameter-free Regret in High Probability with Heavy Tails
We present new algorithms for online convex optimization over unbounded ...
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10/12/2022
Differentially Private Online-to-Batch for Smooth Losses
We develop a new reduction that converts any online convex optimization ...
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10/12/2022
Momentum Aggregation for Private Non-convex ERM
We introduce new algorithms and convergence guarantees for privacy-prese...
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06/27/2022
Long Range Language Modeling via Gated State Spaces
State space models have shown to be effective at modeling long range dep...
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05/13/2022
Optimal Parameter-free Online Learning with Switching Cost
Parameter-freeness in online learning refers to the adaptivity of an alg...
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05/06/2022
Large Scale Transfer Learning for Differentially Private Image Classification
Differential Privacy (DP) provides a formal framework for training machi...
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02/26/2022
Parameter-free Mirror Descent
We develop a modified online mirror descent framework that is suitable f...
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01/19/2022
PDE-Based Optimal Strategy for Unconstrained Online Learning
Unconstrained Online Linear Optimization (OLO) is a practical problem se...
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10/27/2021
Online Selective Classification with Limited Feedback
Motivated by applications to resource-limited and safety-critical domain...
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06/28/2021
High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails
We consider non-convex stochastic optimization using first-order algorit...
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03/04/2021
Correcting Momentum with Second-order Information
We develop a new algorithm for non-convex stochastic optimization that f...
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02/02/2021
Strongly Adaptive OCO with Memory
Recent progress in online control has popularized online learning with m...
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12/24/2020
Upper Confidence Bounds for Combining Stochastic Bandits
We provide a simple method to combine stochastic bandit algorithms. Our ...
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10/06/2020
Online Linear Optimization with Many Hints
We study an online linear optimization (OLO) problem in which the learne...
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08/31/2020
Extreme Memorization via Scale of Initialization
We construct an experimental setup in which changing the scale of initia...
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07/16/2020
Comparator-adaptive Convex Bandits
We study bandit convex optimization methods that adapt to the norm of th...
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02/11/2020
Online Learning with Imperfect Hints
We consider a variant of the classical online linear optimization proble...
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02/10/2020
Adaptive Online Learning with Varying Norms
Given any increasing sequence of norms ·_0,...,·_T-1, we provide an onli...
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02/09/2020
Momentum Improves Normalized SGD
We provide an improved analysis of normalized SGD showing that adding mo...
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05/29/2019
Matrix-Free Preconditioning in Online Learning
We provide an online convex optimization algorithm with regret that inte...
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05/25/2019
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
In this paper, we consider the nonparametric least square regression in ...
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05/24/2019
Momentum-Based Variance Reduction in Non-Convex SGD
Variance reduction has emerged in recent years as a strong competitor to...
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03/03/2019
Anytime Online-to-Batch Conversions, Optimism, and Acceleration
A standard way to obtain convergence guarantees in stochastic convex opt...
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02/24/2019
Artificial Constraints and Lipschitz Hints for Unconstrained Online Learning
We provide algorithms that guarantee regret R_T(u)<Õ(Gu^3 + G(u+1)√(T)) ...
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02/24/2019
Combining Online Learning Guarantees
We show how to take any two parameter-free online learning algorithms wi...
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01/25/2019
Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization
Stochastic Gradient Descent (SGD) has played a central role in machine l...
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02/17/2018
Black-Box Reductions for Parameter-free Online Learning in Banach Spaces
We introduce several new black-box reductions that significantly improve...
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02/16/2018
Distributed Stochastic Optimization via Adaptive Stochastic Gradient Descent
Stochastic convex optimization algorithms are the most popular way to tr...
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03/07/2017
Online Learning Without Prior Information
The vast majority of optimization and online learning algorithms today r...
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03/07/2017
