4 citations · 5 across the 3 of their papers we have counts for
6 papers
A Characterization of Multiclass Learnability
Nataly Brukhim, Daniel Carmon, Irit Dinur +2
A seminal result in learning theory characterizes the PAC learnability of binary classes through the Vapnik-Chervonenkis dimension. Extending this characterization to the general m…
Online Boosting with Bandit Feedback
Nataly Brukhim, Elad Hazan
We consider the problem of online boosting for regression tasks, when only limited information is available to the learner. We give an efficient regret minimization method that has…
Online Agnostic Boosting via Regret Minimization
Nataly Brukhim, Xinyi Chen, Elad Hazan +1
Boosting is a widely used machine learning approach based on the idea of aggregating weak learning rules. While in statistical learning numerous boosting methods exist both in the…
Boosting for Control of Dynamical Systems
Naman Agarwal, Nataly Brukhim, Elad Hazan +1
We study the question of how to aggregate controllers for dynamical systems in order to improve their performance. To this end, we propose a framework of boosting for online contro…
Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning
Valts Blukis, Nataly Brukhim, Andrew Bennett +2
We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands fo…
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Nataly Brukhim, Amir Globerson
Many machine learning problems require the prediction of multi-dimensional labels. Such structured prediction models can benefit from modeling dependencies between labels. Recently…