35 citations · 63 across the 10 of their papers we have counts for
10 papers
Optimal Rates for Differentially Private Hypothesis Testing with E-values
Ben Jacobsen, Tomas Gonzalez, Gavin Brown +2
E-values have attracted considerable interest in recent years as flexible tools for enabling anytime-valid and adaptive data analysis. Hypothesis testing is at the core of many of…
Privately Estimating Monotone Statistics in Polynomial Time
Gavin Brown, Ephraim Linder, Mahbod Majid +1
We study efficient differentially private algorithms for estimating monotone statistics, i.e., statistics that are monotone under the addition of new observations. The starting poi…
Tukey Depth Mechanisms for Practical Private Mean Estimation
Gavin Brown, Lydia Zakynthinou
Mean estimation is a fundamental task in statistics and a focus within differentially private statistical estimation. While univariate methods based on the Gaussian mechanism are w…
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares
Gavin Brown, Jonathan Hayase, Samuel Hopkins +5
We present a sample- and time-efficient differentially private algorithm for ordinary least squares, with error that depends linearly on the dimension and is independent of the con…
Metalearning with Very Few Samples Per Task
Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown +3
Metalearning and multitask learning are two frameworks for solving a group of related learning tasks more efficiently than we could hope to solve each of the individual tasks on th…
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions
Gavin Brown, Samuel B. Hopkins, Adam Smith
We present a fast, differentially private algorithm for high-dimensional covariance-aware mean estimation with nearly optimal sample complexity. Only exponential-time estimators we…
Strong Memory Lower Bounds for Learning Natural Models
Gavin Brown, Mark Bun, Adam Smith
We give lower bounds on the amount of memory required by one-pass streaming algorithms for solving several natural learning problems. In a setting where examples lie in …
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
Gavin Brown, Marco Gaboardi, Adam Smith +2
We present two sample-efficient differentially private mean estimators for -dimensional (sub)Gaussian distributions with unknown covariance. Informally, given …
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?
Gavin Brown, Mark Bun, Vitaly Feldman +2
Modern machine learning models are complex and frequently encode surprising amounts of information about individual inputs. In extreme cases, complex models appear to memorize enti…
Performative Prediction in a Stateful World
Gavin Brown, Shlomi Hod, Iden Kalemaj
Deployed supervised machine learning models make predictions that interact with and influence the world. This phenomenon is called performative prediction by Perdomo et al. (ICML 2…