
The gradient complexity of linear regression
We investigate the computational complexity of several basic linear alge...
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Is Local SGD Better than Minibatch SGD?
We study local SGD (also known as parallel SGD and federated averaging),...
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Implicit Regularization in Matrix Factorization
We study implicit regularization when optimizing an underdetermined quad...
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Tight Complexity Bounds for Optimizing Composite Objectives
We provide tight upper and lower bounds on the complexity of minimizing ...
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The Everlasting Database: Statistical Validity at a Fair Price
The problem of handling adaptivity in data analysis, intentional or not,...
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Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
We suggest a general oraclebased framework that captures different para...
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Training WellGeneralizing Classifiers for Fairness Metrics and Other DataDependent Constraints
Classifiers can be trained with datadependent constraints to satisfy fa...
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The Complexity of Making the Gradient Small in Stochastic Convex Optimization
We give nearly matching upper and lower bounds on the oracle complexity ...
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Kernel and Deep Regimes in Overparametrized Models
A recent line of work studies overparametrized neural networks in the "k...
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Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory
We note that known methods achieving the optimal oracle complexity for f...
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Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis
We design a general framework for answering adaptive statistical queries...
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Lower Bounds for NonConvex Stochastic Optimization
We lower bound the complexity of finding ϵstationary points (with gradi...
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Kernel and Rich Regimes in Overparametrized Models
A recent line of work studies overparametrized neural networks in the "k...
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Mirrorless Mirror Descent: A More Natural Discretization of Riemannian Gradient Flow
We present a direct (primal only) derivation of Mirror Descent as a "par...
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Blake Woodworth
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