Is Randomness Necessary for Adaptive Data Analysis?
The Adaptive Data Analysis (ADA) problem formalizes the challenge of preventing false discovery and overfitting when a dataset is repeatedly reused. Formally, our input is a dataset containing $n$ i.i.d.\ samples from an unknown distribution $P$ over a domain $X$, and our goal is to answer a sequence of $k$ adaptively chosen statistical queries with respect to $P$. The main question is how many queries we can support (i.e., how large $k$ can be), primarily as a function of the number of samples $n$. This question has been intensively studied and is relatively well-understood for randomized mechanisms: there are computationally efficient mechanisms that support $k \approx n^2$ queries, and...
| Authors | Edith Cohen, Haim Kaplan, Yishay Mansour et al. |
|---|---|
| Primary category | cs.CR |
| Published | 8 July 2026 |
| Last updated at source | 22 July 2026 |
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