Publications

Statistical Inference with Stochastic Gradient Algorithms

Stochastic gradient algorithms are widely used for large-scale learning and inference problems. However, their use in practice is …
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Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers

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Approximations of Geometrically Ergodic Reversible Markov Chains

A common tool in the practice of Markov Chain Monte Carlo is to use approximating transition kernels to speed up computation when the …
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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 and Raginsky (2017) provides bounds on the generalization error …
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Relaxing the IID Assumption: Adaptively Minimax Optimal Regret via Root-Entropic Regularization

We consider sequential prediction with expert advice when data are generated from distributions varying arbitrarily within an unknown …
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In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors

We propose to study the generalization error of a learned predictor h^ in terms of that of a surrogate (potentially randomized) …
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Optimal Scaling and Shaping of Random Walk Metropolis via Diffusion Limits of Block-IID Targets

This work extends Roberts et al. (1997) by considering limits of Random Walk Metropolis (RWM) applied to block IID target …
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Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates

In this work, we improve upon the stepwise analysis of noisy iterative learning algorithms initiated by Pensia, Jog, and Loh (2018) and …
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