Yair Carmon | DeepAI

AI Chat

Featured Co-authors

Percy Liang
165 publications
Ali Farhadi
106 publications
Hannaneh Hajishirzi
102 publications
Nathan Srebro
96 publications
Jonathan Berant
84 publications
Aaron Sidford
79 publications
Sewoong Oh
77 publications
Yin Tat Lee
64 publications
Shuran Song
61 publications
Daniel Soudry
49 publications
Ludwig Schmidt
48 publications

∙ 05/22/2023

Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond

Recent research shows that when Gradient Descent (GD) is applied to neur...

0 Itai Kreisler, et al. ∙

∙ 04/27/2023

DataComp: In search of the next generation of multimodal datasets

Large multimodal datasets have been instrumental in recent breakthroughs...

0 Samir Yitzhak Gadre, et al. ∙

∙ 01/01/2023

ReSQueing Parallel and Private Stochastic Convex Optimization

We introduce a new tool for stochastic convex optimization (SCO): a Rewe...

0 Yair Carmon, et al. ∙

∙ 11/28/2022

Malign Overfitting: Interpolation Can Provably Preclude Invariance

Learned classifiers should often possess certain invariance properties m...

0 Yoav Wald, et al. ∙

∙ 06/17/2022

RECAPP: Crafting a More Efficient Catalyst for Convex Optimization

The accelerated proximal point algorithm (APPA), also known as "Catalyst...

0 Yair Carmon, et al. ∙

∙ 05/30/2022

Optimal and Adaptive Monteiro-Svaiter Acceleration

We develop a variant of the Monteiro-Svaiter (MS) acceleration framework...

0 Yair Carmon, et al. ∙

∙ 05/04/2022

Making SGD Parameter-Free

We develop an algorithm for parameter-free stochastic convex optimizatio...

0 Yair Carmon, et al. ∙

∙ 03/24/2022

Distributionally Robust Optimization via Ball Oracle Acceleration

We develop and analyze algorithms for distributionally robust optimizati...

0 Yair Carmon, et al. ∙

∙ 03/10/2022

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

The conventional recipe for maximizing model accuracy is to (1) train mu...

10 Mitchell Wortsman, et al. ∙

∙ 02/13/2022

Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments

Neural scaling laws define a predictable relationship between a model's ...

0 Maor Ivgi, et al. ∙

∙ 07/09/2021

Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

For machine learning systems to be reliable, we must understand their pe...

0 John Miller, et al. ∙

∙ 06/29/2021

Never Go Full Batch (in Stochastic Convex Optimization)

We study the generalization performance of full-batch optimization algor...

0 Idan Amir, et al. ∙

∙ 06/17/2021

Stochastic Bias-Reduced Gradient Methods

We develop a new primitive for stochastic optimization: a low-bias, low-...

0 Hilal Asi, et al. ∙

∙ 05/04/2021

Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss

We characterize the complexity of minimizing max_i∈[N] f_i(x) for convex...

0 Yair Carmon, et al. ∙

∙ 10/12/2020

Large-Scale Methods for Distributionally Robust Optimization

We propose and analyze algorithms for distributionally robust optimizati...

0 Daniel Lévy, et al. ∙

∙ 09/17/2020

Coordinate Methods for Matrix Games

We develop primal-dual coordinate methods for solving bilinear saddle-po...

0 Yair Carmon, et al. ∙

∙ 06/24/2020

Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations

We design an algorithm which finds an ϵ-approximate stationary point (wi...

0 Yossi Arjevani, et al. ∙

∙ 03/18/2020

Acceleration with a Ball Optimization Oracle

Consider an oracle which takes a point x and returns the minimizer of a ...

0 Yair Carmon, et al. ∙

∙ 12/05/2019

Lower Bounds for Non-Convex Stochastic Optimization

We lower bound the complexity of finding ϵ-stationary points (with gradi...

0 Yossi Arjevani, et al. ∙

∙ 07/03/2019

Variance Reduction for Matrix Games

We present a randomized primal-dual algorithm that solves the problem _x...

0 Yair Carmon, et al. ∙

∙ 05/31/2019

Unlabeled Data Improves Adversarial Robustness

We demonstrate, theoretically and empirically, that adversarial robustne...

0 Yair Carmon, et al. ∙

∙ 03/07/2019

A Rank-1 Sketch for Matrix Multiplicative Weights

We show that a simple randomized sketch of the matrix multiplicative wei...

0 Yair Carmon, et al. ∙

∙ 05/26/2016

No bad local minima: Data independent training error guarantees for multilayer neural networks

We use smoothed analysis techniques to provide guarantees on the trainin...

0 Daniel Soudry, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world