7 citations · 16 across the 8 of their papers we have counts for
13 papers
Never Go Full Batch (in Stochastic Convex Optimization)
Idan Amir, Yair Carmon, Tomer Koren +1
We study the generalization performance of optimization algorithms for stochastic convex optimization: these are first-order methods that only access the exact…
Littlestone Classes are Privately Online Learnable
Noah Golowich, Roi Livni
We consider the problem of online classification under a privacy constraint. In this setting a learner observes sequentially a stream of labelled examples , for $1 \leq…
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games
Steve Hanneke, Roi Livni, Shay Moran
Which classes can be learned properly in the online model? -- that is, by an algorithm that at each round uses a predictor from the concept class. While there are simple and natura…
SGD Generalizes Better Than GD (And Regularization Doesn't Help)
Idan Amir, Tomer Koren, Roi Livni
We give a new separation result between the generalization performance of stochastic gradient descent (SGD) and of full-batch gradient descent (GD) in the fundamental stochastic co…
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study
Assaf Dauber, Meir Feder, Tomer Koren +1
The notion of implicit bias, or implicit regularization, has been suggested as a means to explain the surprising generalization ability of modern-days overparameterized learning al…
Graph-based Discriminators: Sample Complexity and Expressiveness
Roi Livni, Yishay Mansour
A basic question in learning theory is to identify if two distributions are identical when we have access only to examples sampled from the distributions. This basic task is consid…