Mittagsseminar (in cooperation with J. Lengler, A. Steger, and D. Steurer)

Date and Time: Friday, October 17, 2025, 03:00 pm

Duration: 30 minutes

Location: OAT S15 (15:00)

Speaker: Matt Regehr (University of Waterloo)

Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph

We propose an algorithm with improved query-complexity for the problem of hypothesis selection under local differential privacy constraints. Given a set of k probability distributions Q, we describe an algorithm that satisfies local differential privacy, performs ~O(k^3/2) non-adaptive queries to individuals who each have samples from a probability distribution p, and outputs a probability distribution from the set Q which is nearly the closest to p. Previous algorithms required either Ω(k^2) queries or many rounds of interactive queries. Technically, we introduce a new object we dub the Scheffé graph, which captures structure of the differences between distributions in Q, and may be of more broad interest for hypothesis selection tasks.

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