113 citations · 241 across the 31 of their papers we have counts for
7 papers · 1 filter
Efficient Agnostic Learning of Univariate Densities Under Local Differential Privacy
Mohammad Afzali, Hassan Ashtiani, Sivan Sabato
We propose a general framework for non-interactive locally differentially private (LDP) learning of structured univariate distributions that can be approximated by piece-wise polyn…
Fast Single-Class Classification and the Principle of Logit Separation
Gil Keren, Sivan Sabato, Björn Schuller
We consider neural network training, in applications in which there are many possible classes, but at test-time, the task is a binary classification task of determining whether the…
Tunable Sensitivity to Large Errors in Neural Network Training
Gil Keren, Sivan Sabato, Björn Schuller
When humans learn a new concept, they might ignore examples that they cannot make sense of at first, and only later focus on such examples, when they are more useful for learning.…
Interactive algorithms: from pool to stream
Sivan Sabato, Tom Hess
We consider interactive algorithms in the pool-based setting, and in the stream-based setting. Interactive algorithms observe suggested elements (representing actions or queries),…
Active Regression by Stratification
Sivan Sabato, Remi Munos
We propose a new active learning algorithm for parametric linear regression with random design. We provide finite sample convergence guarantees for general distributions in the mis…
Learning Sparse Low-Threshold Linear Classifiers
Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro +2
We consider the problem of learning a non-negative linear classifier with a -norm of at most , and a fixed threshold, under the hinge-loss. This problem generalizes the probl…
Distribution-Dependent Sample Complexity of Large Margin Learning
Sivan Sabato, Nathan Srebro, Naftali Tishby
We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L2 regularization: We introduce the margin-adapted dimension,…