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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning.

Aurélien Bibaut¹, Nathan Kallus², Maria Dimakopoulou¹

¹Netflix.

Advances in Neural Information Processing Systems

|January 2, 2023

Summary

This summary is machine-generated.

Empirical risk minimization (ERM) using adaptively collected data, like from contextual bandits, is improved with a new weighted ERM algorithm. This provides novel generalization guarantees and faster convergence for machine learning tasks.

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Area of Science:

Machine Learning
Reinforcement Learning
Statistical Learning Theory

Background:

Empirical risk minimization (ERM) is a fundamental machine learning technique.
Standard ERM guarantees can be unreliable with adaptively collected data, such as from contextual bandit algorithms.
Existing methods lack robust theoretical guarantees for ERM with adaptive data.

Purpose of the Study:

To develop and analyze a novel importance sampling weighted ERM algorithm for adaptively collected data.
To establish first-of-their-kind generalization guarantees and convergence rates for this algorithm.
To address limitations in policy learning and regression with adaptive data collection.

Main Methods:

A generic importance sampling weighted ERM algorithm is proposed.
A new maximal inequality is derived, leveraging the importance sampling structure.
The algorithm is analyzed for both regression and policy learning settings.

Main Results:

The proposed algorithm achieves generalization guarantees and fast convergence rates for ERM with adaptive data.
Specific fast rates are derived for regression, utilizing the strong convexity of squared-error loss.
Regret guarantees are provided for policy learning, closing an open gap in the literature for decaying exploration.

Conclusions:

The developed weighted ERM algorithm offers improved performance and theoretical guarantees for machine learning with adaptively collected data.
The findings have significant implications for contextual bandit problems and off-policy policy learning.
Empirical validation supports the theoretical advancements presented in the study.