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Yossi Matias
Are All Distributions Easy?
An Introduction to Johnson–Lindenstrauss Transforms
Adversarial Robustness of Streaming Algorithms Through Importance Sampling
Private Center Points and Learning of Halfspaces
Adversarially Robust Property-Preserving Hash Functions
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification
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On Adaptive Distance Estimation
Optimal Bounds for Estimating Entropy with PMF Queries
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Federated Computing Research Conference, FCRC’96, Which Is David Wise, Steering Being Held May 20 - 28, 1996 at the Philadelphia Downtown Marriott
Simultaneous Private Learning of Multiple Concepts∗
Differentially Private Approximations of a Convex Hull in Low Dimensions
Testing Closeness of Discrete Distributions
A Near-Optimal Algorithm for L1-Difference
Goldwasser Transcript Final with Timestamps)
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles
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Fast Moment Estimation in Data Streams in Optimal Space
DROP: a Workload-Aware Optimizer for Dimensionality Reduction
Novel Frameworks for Mining Heterogeneous and Dynamic
Card Guessing with Limited Memory∗
Model Counting Meets F0 Estimation
On Adaptive Distance Estimation
A Framework for Adversarial Streaming Via Differential Privacy
Efficient and Private Distance Approximation in The
Return of Organization Exempt from Income
New Directions in Bandit Learning: Singularities and Random Walk Feedback by Tianyu Wang
Bounded Independence Fools Degree-2 Threshold Functions
Simple Analysis of Johnson-Lindenstrauss Transform Under Neuroscience Constraints Maciej Skorski University of Luxembourg
Annual Report of the ACM Awards Committee for the Period July 1, 2009 - June 30, 2010
Property-Preserving Hash Functions from Standard Assumptions
Batched Differentially Private Information Retrieval 1 Introduction
Association for Computing Machinery 2 Penn Plaza, Suite 701, New York
Regular Programming Over Data Streams