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Relaxed Models for Adversarial Streaming: The Advice Model and the Bounded Interruptions Model
Streaming algorithms are typically analyzed in the oblivious setting, where we assume that the input stream is fixed in advance.
On the Robustness of CountSketch to Adaptive Inputs
CountSketch is a popular dimensionality reduction technique that maps vectors to a lower dimension using randomized linear measurements.
A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators
Classical streaming algorithms operate under the (not always reasonable) assumption that the input stream is fixed in advance.
Differentially Private Algorithms for Clustering with Stability Assumptions
We study the problem of differentially private clustering under input-stability assumptions.
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