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Found 14 papers, 1 papers with code
Efficient Active Learning of Halfspaces: an Aggressive Approach
Our efficient aggressive active learner of half-spaces has formal approximation guarantees that hold when the pool is separable with a margin.
Subspace Learning with Partial Information
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.
Strongly Adaptive Online Learning
Strongly adaptive algorithms are algorithms whose performance on every time interval is close to optimal.
Faster SGD Using Sketched Conditioning
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.
Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization
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.
Solving Ridge Regression using Sketched Preconditioned SVRG
We develop a novel preconditioning method for ridge regression, based on recent linear sketching methods.
Fast Rates for Empirical Risk Minimization of Strict Saddle Problems
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.
Smooth Sensitivity Based Approach for Differentially Private Principal Component Analysis
The problem of designing simpler and more efficient methods for this task has been raised as an open problem in \cite{kapralov2013differentially}.
Optimal Sketching Bounds for Exp-concave Stochastic Minimization
We derive optimal statistical and computational complexity bounds for exp-concave stochastic minimization in terms of the effective dimension.
Learning in Non-convex Games with an Optimization Oracle
We consider online learning in an adversarial, non-convex setting under the assumption that the learner has an access to an offline optimization oracle.
Private Learning Implies Online Learning: An Efficient Reduction
We study the relationship between the notions of differentially private learning and online learning in games.
Boosting Simple Learners
(ii) Expressivity: Which tasks can be learned by boosting weak hypotheses from a bounded VC class?
Towards a combinatorial characterization of bounded memory learning
In this paper we aim to develop combinatorial dimensions that characterize bounded memory learning.
Towards a Combinatorial Characterization of Bounded-Memory Learning
We propose a candidate solution for the case of realizable strong learning under a known distribution, based on the SQ dimension of neighboring distributions.
