28 citations · 174 across the 52 of their papers we have counts for
8 papers · 1 filter
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…
Generalized Private Selection and Testing with High Confidence
Edith Cohen, Xin Lyu, Jelani Nelson +2
Composition theorems are general and powerful tools that facilitate privacy accounting across multiple data accesses from per-access privacy bounds. However they often result in we…
Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs
Edith Cohen, Jelani Nelson, Tamás Sarlós +1
CountSketch and Feature Hashing (the "hashing trick") are popular randomized dimensionality reduction methods that support recovery of -heavy hitters (keys where $v_i^2…
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably
Bar Alon, Moni Naor, Eran Omri +1
In this work, we introduce the Gulliver multi-party computation model (GMPC). The GMPC model considers a single highly powerful party, called the server or Gulliver, that is connec…
On the Robustness of CountSketch to Adaptive Inputs
Edith Cohen, Xin Lyu, Jelani Nelson +3
CountSketch is a popular dimensionality reduction technique that maps vectors to a lower dimension using randomized linear measurements. The sketch supports recovering -hea…