1 citations · 1 across the 10 of their papers we have counts for
13 papers
Locally Sparsified, Globally Near-Optimal: Matching under Independent Vertex Arrivals
Sara Ahmadian, Edith Cohen, Mohammad Roghani
Resource allocation systems often restrict each request to a short list of options before coordinating assignments globally. We study this separation in stochastic bipartite matchi…
Tight Bounds on the Cost of Adaptivity for the Meyerson Sketch
Edith Cohen, Elena Gribelyuk, Pasin Manurangsi +1
In online facility location, points arrive one at a time, and the algorithm must either open a facility at the arriving point or route the point to an existing facility. The Meyers…
Stochastic Matching via Local Sparsification
Sara Ahmadian, Edith Cohen, Mohammad Roghani
The classic online stochastic matching problem typically requires immediate and irrevocable matching decisions. However, in many modern decentralized systems such as real-time ride…
Adaptively Robust Resettable Streaming
Edith Cohen, Elena Gribelyuk, Jelani Nelson +1
We study algorithms in the resettable streaming model, where the value of each key can either be increased or reset to zero. The model is suitable for applications such as active r…
A Simple and Robust Protocol for Distributed Counting
Edith Cohen, Moshe Shechner, Uri Stemmer
We revisit the distributed counting problem, where a server must continuously approximate the total number of events occurring across sites while minimizing communication. The…
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…
Tight Bounds for Answering Adaptively Chosen Concentrated Queries
Emma Rapoport, Edith Cohen, Uri Stemmer
Most work on adaptive data analysis assumes that samples in the dataset are independent. When correlations are allowed, even the non-adaptive setting can become intractable, unless…
Breaking the Quadratic Barrier: Robust Cardinality Sketches for Adaptive Queries
Edith Cohen, Mihir Singhal, Uri Stemmer
Cardinality sketches are compact data structures that efficiently estimate the number of distinct elements across multiple queries while minimizing storage, communication, and comp…
One Attack to Rule Them All: Tight Quadratic Bounds for Adaptive Queries on Cardinality Sketches
Edith Cohen, Jelani Nelson, Tamás Sarlós +2
Cardinality sketches are compact data structures for representing sets or vectors. These sketches are space-efficient, typically requiring only logarithmic storage in the input siz…
Unmasking Vulnerabilities: Cardinality Sketches under Adaptive Inputs
Sara Ahmadian, Edith Cohen
Cardinality sketches are popular data structures that enhance the efficiency of working with large data sets. The sketches are randomized representations of sets that are only of l…
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries
Edith Cohen, Xin Lyu, Jelani Nelson +2
One of the most basic problems for studying the "price of privacy over time" is the so called private counter problem, introduced by Dwork et al. (2010) and Chan et al. (2010). In…
Hot PATE: Private Aggregation of Distributions for Diverse Task
Edith Cohen, Benjamin Cohen-Wang, Xin Lyu +3
The Private Aggregation of Teacher Ensembles (PATE) framework enables privacy-preserving machine learning by aggregating responses from disjoint subsets of sensitive data. Adaptati…
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…