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Found 6 papers, 1 papers with code
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.
Differentially-Private Bayes Consistency
We construct a universally Bayes consistent learning rule that satisfies differential privacy (DP).
Adaptive Data Analysis with Correlated Observations
The vast majority of the work on adaptive data analysis focuses on the case where the samples in the dataset are independent.
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles
We revisit the fundamental problem of learning Axis-Aligned-Rectangles over a finite grid $X^d\subseteq{\mathbb{R}}^d$ with differential privacy.
Agnostic Sample Compression Schemes for Regression
For the $\ell_2$ loss, does every function class admit an approximate compression scheme of polynomial size in the fat-shattering dimension?
Sample Compression for Real-Valued Learners
We give an algorithmically efficient version of the learner-to-compression scheme conversion in Moran and Yehudayoff (2016).
