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Algorithmic stability for adaptive data analysis

Published: 19 June 2016 Publication History
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  • Abstract

    Adaptivity is an important feature of data analysis - the choice of questions to ask about a dataset often depends on previous interactions with the same dataset. However, statistical validity is typically studied in a nonadaptive model, where all questions are specified before the dataset is drawn. Recent work by Dwork et al. (STOC, 2015) and Hardt and Ullman (FOCS, 2014) initiated a general formal study of this problem, and gave the first upper and lower bounds on the achievable generalization error for adaptive data analysis.
    Specifically, suppose there is an unknown distribution P and a set of n independent samples x is drawn from P. We seek an algorithm that, given x as input, accurately answers a sequence of adaptively chosen ``queries'' about the unknown distribution P. How many samples n must we draw from the distribution, as a function of the type of queries, the number of queries, and the desired level of accuracy?
    In this work we make two new contributions towards resolving this question:
    We give upper bounds on the number of samples n that are needed to answer statistical queries. The bounds improve and simplify the work of Dwork et al. (STOC, 2015), and have been applied in subsequent work by those authors (Science, 2015; NIPS, 2015).
    We prove the first upper bounds on the number of samples required to answer more general families of queries. These include arbitrary low-sensitivity queries and an important class of optimization queries (alternatively, risk minimization queries).
    As in Dwork et al., our algorithms are based on a connection with algorithmic stability in the form of differential privacy. We extend their work by giving a quantitatively optimal, more general, and simpler proof of their main theorem that the stability notion guaranteed by differential privacy implies low generalization error. We also show that weaker stability guarantees such as bounded KL divergence and total variation distance lead to correspondingly weaker generalization guarantees.

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    cover image ACM Conferences
    STOC '16: Proceedings of the forty-eighth annual ACM symposium on Theory of Computing
    June 2016
    1141 pages
    ISBN:9781450341325
    DOI:10.1145/2897518
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 19 June 2016

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    Author Tags

    1. Adaptivity
    2. Data Analysis
    3. Differential Privacy
    4. Generalization
    5. Stability
    6. Statistics

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    STOC '16: Symposium on Theory of Computing
    June 19 - 21, 2016
    MA, Cambridge, USA

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    Overall Acceptance Rate 1,469 of 4,586 submissions, 32%

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    • (2024)Publishing Common Neighbors Histograms of Social Networks under Edge Differential PrivacyProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3637646(1099-1113)Online publication date: 1-Jul-2024
    • (2024)Total Variation Meets Differential PrivacyIEEE Journal on Selected Areas in Information Theory10.1109/JSAIT.2024.33840835(207-220)Online publication date: 2024
    • (2023)Characterization of overfitting in robust multiclass classificationProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669467(76547-76557)Online publication date: 10-Dec-2023
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    • (2023)Post-selection inference via algorithmic stabilityThe Annals of Statistics10.1214/23-AOS230351:4Online publication date: 1-Aug-2023
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