Peter Bartlett
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| title | conference | date |
|---|---|---|
| Benign Overfitting in Linear Prediction | Frontiers of Deep Learning 2019 | 2019-07-15 |
Shows that linear regression can benignly overfit: the minimum-norm interpolator (pseudoinverse solution) attains vanishing excess risk when the covariance… | ||
| Generalization I | Deep Learning Boot Camp 2019 | 2019-05-28 |
Proves a concrete generalization claim: the perceptron update makes at most 1/γ^2 mistake bound on any sequence with margin γ, and a one‑pass run yields… | ||
| Generalization II | Deep Learning Boot Camp 2019 | 2019-05-28 |
Shows that uniform deviation between population and empirical losses is essentially captured by Rademacher averages using symmetrization with a ghost sample… | ||
| Generalization III | Deep Learning Boot Camp 2019 | 2019-05-28 |
Uses Rademacher symmetrization to reduce uniform deviations to analyzable averages and gives classification bounds like sup deviation = O(sqrt(d log n / n))… | ||
| Generalization IV | Deep Learning Boot Camp 2019 | 2019-05-28 |
Compares K‑nearest neighbors and kernel smoothing with explicit bias–variance decompositions, deriving the minimax rate N^{-2/(2+d)} under Lipschitz… | ||
| Efficient Minimax Optimal Strategies for Universal Prediction | Information Theory, Learning and Big Data (Simons Institute, 2015) | 2015-03-16 |
Shows when the minimax-optimal strategy is computable and what it actually is: a dynamic-program recursion yields normalized maximum likelihood as the unique… | ||
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