1 code implementation • 31 Jan 2020 • Noga Alon, Alon Gonen, Elad Hazan, Shay Moran
(ii) Expressivity: Which tasks can be learned by boosting weak hypotheses from a bounded VC class?
no code implementations • 21 May 2018 • Naman Agarwal, Alon Gonen
We derive optimal statistical and computational complexity bounds for exp-concave stochastic minimization in terms of the effective dimension.
no code implementations • 29 Oct 2017 • Ran Gilad-Bachrach, Alon Gonen
The problem of designing simpler and more efficient methods for this task has been raised as an open problem in \cite{kapralov2013differentially}.
no code implementations • 16 Jan 2017 • Alon Gonen, Shai Shalev-Shwartz
We derive bounds on the sample complexity of empirical risk minimization (ERM) in the context of minimizing non-convex risks that admit the strict saddle property.
no code implementations • 15 Jan 2016 • Alon Gonen, Shai Shalev-Shwartz
We show that the average stability notion introduced by \cite{kearns1999algorithmic, bousquet2002stability} is invariant to data preconditioning, for a wide class of generalized linear models that includes most of the known exp-concave losses.
no code implementations • 19 Feb 2014 • Alon Gonen, Dan Rosenbaum, Yonina Eldar, Shai Shalev-Shwartz
The goal of subspace learning is to find a $k$-dimensional subspace of $\mathbb{R}^d$, such that the expected squared distance between instance vectors and the subspace is as small as possible.
no code implementations • 7 Feb 2016 • Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz
We develop a novel preconditioning method for ridge regression, based on recent linear sketching methods.
no code implementations • 25 Feb 2015 • Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
Strongly adaptive algorithms are algorithms whose performance on every time interval is close to optimal.
no code implementations • 8 Jun 2015 • Alon Gonen, Shai Shalev-Shwartz
We propose a novel method for speeding up stochastic optimization algorithms via sketching methods, which recently became a powerful tool for accelerating algorithms for numerical linear algebra.
no code implementations • 17 Aug 2012 • Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz
Our efficient aggressive active learner of half-spaces has formal approximation guarantees that hold when the pool is separable with a margin.
no code implementations • 17 Oct 2018 • Naman Agarwal, Alon Gonen, Elad Hazan
We consider online learning in an adversarial, non-convex setting under the assumption that the learner has an access to an offline optimization oracle.
no code implementations • NeurIPS 2019 • Alon Gonen, Elad Hazan, Shay Moran
We study the relationship between the notions of differentially private learning and online learning in games.
no code implementations • 8 Feb 2020 • Alon Gonen, Shachar Lovett, Michal Moshkovitz
In this paper we aim to develop combinatorial dimensions that characterize bounded memory learning.
no code implementations • NeurIPS 2020 • Alon Gonen, Shachar Lovett, Michal Moshkovitz
We propose a candidate solution for the case of realizable strong learning under a known distribution, based on the SQ dimension of neighboring distributions.