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cs.DS2026

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…

cs.DS2026

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…

cs.DS2025

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…

cs.DS2025

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…

cs.DS2025

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…