1 citations · 1 across the 5 of their papers we have counts for
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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…
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…
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles
Menachem Sadigurschi, Uri Stemmer
We revisit the fundamental problem of learning Axis-Aligned-Rectangles over a finite grid with differential privacy. Existing results show that the sam…
Agnostic Sample Compression Schemes for Regression
Idan Attias, Steve Hanneke, Aryeh Kontorovich +1
We obtain the first positive results for bounded sample compression in the agnostic regression setting with the loss, where . We construct a generic appro…
Sample Compression for Real-Valued Learners
Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
We give an algorithmically efficient version of the learner-to-compression scheme conversion in Moran and Yehudayoff (2016). In extending this technique to real-valued hypotheses,…