26 citations · 63 across the 20 of their papers we have counts for
17 papers · 1 filter
Protecting the Undeleted in Machine Unlearning
Aloni Cohen, Refael Kohen, Kobbi Nissim +1
Machine unlearning aims to remove specific data points from a trained model, often striving to emulate "perfect retraining", i.e., producing the model that would have been obtained…
The Cost of Compression: Tight Quadratic Black-Box Attacks on Sketches for Norm Estimation
Sara Ahmadian, Edith Cohen, Uri Stemmer
Dimensionality reduction via linear sketching is a powerful and widely used technique, but it is known to be vulnerable to adversarial inputs. We study the black-box adversarial se…
Nearly Optimal Sample Complexity for Learning with Label Proportions
Robert Busa-Fekete, Travis Dick, Claudio Gentile +3
We investigate Learning from Label Proportions (LLP), a partial information setting where examples in a training set are grouped into bags, and only aggregate label values in each…
Differentially-Private Bayes Consistency
Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich +4
We construct a universally Bayes consistent learning rule that satisfies differential privacy (DP). We first handle the setting of binary classification and then extend our rule to…
Õptimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization
Edith Cohen, Xin Lyu, Jelani Nelson +2
The problem of learning threshold functions is a fundamental one in machine learning. Classical learning theory implies sample complexity of (for generalizati…
Adaptive Data Analysis with Correlated Observations
Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer
The vast majority of the work on adaptive data analysis focuses on the case where the samples in the dataset are independent. Several approaches and tools have been successfully ap…