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05/22/2023
Faster Differentially Private Convex Optimization via Second-Order Methods
Differentially private (stochastic) gradient descent is the workhorse of...
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02/19/2023
Why Is Public Pretraining Necessary for Private Model Training?
In the privacy-utility tradeoff of a model trained on benchmark language...
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12/27/2022
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization
To date, no "information-theoretic" frameworks for reasoning about gener...
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06/29/2022
Understanding Generalization via Leave-One-Out Conditional Mutual Information
We study the mutual information between (certain summaries of) the outpu...
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11/09/2021
Towards a Unified Information-Theoretic Framework for Generalization
In this work, we investigate the expressiveness of the "conditional mutu...
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11/05/2020
On the Information Complexity of Proper Learners for VC Classes in the Realizable Case
We provide a negative resolution to a conjecture of Steinke and Zakynthi...
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04/27/2020
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms
The information-theoretic framework of Russo and J. Zou (2016) and Xu an...
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12/03/2019
Sequential Classification with Empirically Observed Statistics
Motivated by real-world machine learning applications, we consider a sta...
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11/06/2019
