113 citations · 241 across the 31 of their papers we have counts for
35 papers
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…
Discriminative Feature Feedback with General Teacher Classes
Omri Bar Oz, Tosca Lechner, Sivan Sabato
We study the theoretical properties of the interactive learning protocol Discriminative Feature Feedback (DFF) (Dasgupta et al., 2018). The DFF learning protocol uses feedback in t…
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies
Shlomi Weitzman, Sivan Sabato
We study adaptive combinatorial maximization, which is a core challenge in machine learning, with applications in active learning as well as many other domains. We study the Bayesi…
On the Capacity Limits of Privileged ERM
Michal Sharoni, Sivan Sabato
We study the supervised learning paradigm called Learning Using Privileged Information, first suggested by Vapnik and Vashist (2009). In this paradigm, in addition to the examples…
Improved Robust Algorithms for Learning with Discriminative Feature Feedback
Sivan Sabato
Discriminative Feature Feedback is a setting proposed by Dastupta et al. (2018), which provides a protocol for interactive learning based on feature explanations that are provided…
Disparate Conditional Prediction in Multiclass Classifiers
Sivan Sabato, Eran Treister, Elad Yom-Tov
We propose methods for auditing multiclass classifiers for fairness under multiclass equalized odds,by estimating the deviation from equalized odds when the classifier is not compl…
Fast Distributed k-Means with a Small Number of Rounds
Tom Hess, Ron Visbord, Sivan Sabato
We propose a new algorithm for k-means clustering in a distributed setting, where the data is distributed across many machines, and a coordinator communicates with these machines t…
A Fast Algorithm for PAC Combinatorial Pure Exploration
Noa Ben-David, Sivan Sabato
We consider the problem of Combinatorial Pure Exploration (CPE), which deals with finding a combinatorial set or arms with a high reward, when the rewards of individual arms are un…
Active Structure Learning of Bayesian Networks in an Observational Setting
Noa Ben-David, Sivan Sabato
We study active structure learning of Bayesian networks in an observational setting, in which there are external limitations on the number of variable values that can be observed f…
A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering
Tom Hess, Michal Moshkovitz, Sivan Sabato
We study k-median clustering under the sequential no-substitution setting. In this setting, a data stream is sequentially observed, and some of the points are selected by the algor…
Active Feature Selection for the Mutual Information Criterion
Shachar Schnapp, Sivan Sabato
We study active feature selection, a novel feature selection setting in which unlabeled data is available, but the budget for labels is limited, and the examples to label can be ac…
Testing Product Distributions: A Closer Look
Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy +1
We study the problems of identity and closeness testing of -dimensional product distributions. Prior works by Canonne, Diakonikolas, Kane and Stewart (COLT 2017) and Daskalakis…
Approximating a Target Distribution using Weight Queries
Nadav Barak, Sivan Sabato
We consider a novel challenge: approximating a distribution without the ability to randomly sample from that distribution. We study how such an approximation can be obtained using…
Robust Learning from Discriminative Feature Feedback
Sanjoy Dasgupta, Sivan Sabato
Recent work introduced the model of learning from discriminative feature feedback, in which a human annotator not only provides labels of instances, but also identifies discriminat…
Universal Bayes consistency in metric spaces
Steve Hanneke, Aryeh Kontorovich, Sivan Sabato +1
We extend a recently proposed 1-nearest-neighbor based multiclass learning algorithm and prove that our modification is universally strongly Bayes-consistent in all metric spaces a…
Sequential no-Substitution k-Median-Clustering
Tom Hess, Sivan Sabato
We study the sample-based k-median clustering objective under a sequential setting without substitutions. In this setting, an i.i.d. sequence of examples is observed. An example ca…
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…
Temporal anomaly detection: calibrating the surprise
Eyal Gutflaish, Aryeh Kontorovich, Sivan Sabato +2
We propose a hybrid approach to temporal anomaly detection in access data of users to databases --- or more generally, any kind of subject-object co-occurrence data. We consider a…
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions
Aryeh Kontorovich, Sivan Sabato, Roi Weiss
We examine the Bayes-consistency of a recently proposed 1-nearest-neighbor-based multiclass learning algorithm. This algorithm is derived from sample compression bounds and enjoys…
Selecting with History
Tom Hess, Sivan Sabato
We define a new selection problem, \emph{Selecting with History}, which extends the secretary problem to a setting with historical information. We propose a strategy for this probl…
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.…
Active Nearest-Neighbor Learning in Metric Spaces
Aryeh Kontorovich, Sivan Sabato, Ruth Urner
We propose a pool-based non-parametric active learning algorithm for general metric spaces, called MArgin Regularized Metric Active Nearest Neighbor (MARMANN), which outputs a near…
Submodular Learning and Covering with Response-Dependent Costs
Sivan Sabato
We consider interactive learning and covering problems, in a setting where actions may incur different costs, depending on the response to the action. We propose a natural greedy a…
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),…