Satyen Kale | DeepAI

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∙ 08/20/2023

Improved Differentially Private Densest Subgraph: Local and Purely Additive

We study the Densest Subgraph problem under the additional constraint of...

0 Michael Dinitz, et al. ∙

∙ 10/13/2022

From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent

Stochastic Gradient Descent (SGD) has been the method of choice for lear...

0 Satyen Kale, et al. ∙

∙ 07/06/2022

Private Matrix Approximation and Geometry of Unitary Orbits

Consider the following optimization problem: Given n × n matrices A and ...

0 Oren Mangoubi, et al. ∙

∙ 05/26/2022

Mixed Federated Learning: Joint Decentralized and Centralized Learning

Federated learning (FL) enables learning from decentralized privacy-sens...

0 Sean Augenstein, et al. ∙

∙ 02/09/2022

Reproducibility in Optimization: Theoretical Framework and Limits

We initiate a formal study of reproducibility in optimization. We define...

5 Kwangjun Ahn, et al. ∙

∙ 02/06/2022

Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States

We revisit the classical online portfolio selection problem. It is widel...

2 Julian Zimmert, et al. ∙

∙ 01/31/2022

Agnostic Learnability of Halfspaces via Logistic Loss

We investigate approximation guarantees provided by logistic regression ...

0 Ziwei Ji, et al. ∙

∙ 10/06/2021

Efficient Methods for Online Multiclass Logistic Regression

Multiclass logistic regression is a fundamental task in machine learning...

0 Naman Agarwal, et al. ∙

∙ 07/11/2021

SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs

Multi-epoch, small-batch, Stochastic Gradient Descent (SGD) has been the...

0 Satyen Kale, et al. ∙

∙ 03/01/2021

A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness

Alongside the well-publicized accomplishments of deep neural networks th...

0 Jacob Abernethy, et al. ∙

∙ 02/23/2021

Learning with User-Level Privacy

We propose and analyze algorithms to solve a range of learning tasks und...

5 Daniel Lévy, et al. ∙

∙ 08/08/2020

Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Federated learning is a challenging optimization problem due to the hete...

0 Sai Praneeth Karimireddy, et al. ∙

∙ 02/19/2020

Estimating Training Data Influence by Tracking Gradient Descent

We introduce a method called TrackIn that computes the influence of a tr...

13 Garima Pruthi, et al. ∙

∙ 02/04/2020

A Deep Conditioning Treatment of Neural Networks

We study the role of depth in training randomly initialized overparamete...

0 Naman Agarwal, et al. ∙

∙ 10/14/2019

SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning

Federated learning is a key scenario in modern large-scale machine learn...

7 Sai Praneeth Karimireddy, et al. ∙

∙ 04/19/2019

On the Convergence of Adam and Beyond

Several recently proposed stochastic optimization methods that have been...

30 Sashank J Reddi, et al. ∙

∙ 04/09/2019

Hypothesis Set Stability and Generalization

We present an extensive study of generalization for data-dependent hypot...

0 Dylan J. Foster, et al. ∙

∙ 01/26/2019

Escaping Saddle Points with Adaptive Gradient Methods

Adaptive methods such as Adam and RMSProp are widely used in deep learni...

0 Matthew Staib, et al. ∙

∙ 10/16/2018

Stochastic Negative Mining for Learning with Large Output Spaces

We consider the problem of retrieving the most relevant labels for a giv...

0 Sashank J Reddi, et al. ∙

∙ 03/25/2018

Logistic Regression: The Importance of Being Improper

Learning linear predictors with the logistic loss---both in stochastic a...

0 Dylan J. Foster, et al. ∙

∙ 12/30/2017

Parameter-free online learning via model selection

We introduce an efficient algorithmic framework for model selection in o...

0 Dylan J. Foster, et al. ∙

∙ 02/04/2014

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

We present a new algorithm for the contextual bandit learning problem, w...

0 Alekh Agarwal, et al. ∙

∙ 04/22/2013

Bargaining for Revenue Shares on Tree Trading Networks

We study trade networks with a tree structure, where a seller with a sin...

0 Arpita Ghosh, et al. ∙

∙ 06/27/2012

Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization

We describe novel subgradient methods for a broad class of matrix optimi...

0 Haim Avron, et al. ∙

∙ 06/13/2011

Efficient Optimal Learning for Contextual Bandits

We address the problem of learning in an online setting where the learne...

0 Miroslav Dudík, et al. ∙

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