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Found 11 papers, 1 papers with code
Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization
In this work, we investigate the interplay between memorization and learning in the context of \emph{stochastic convex optimization} (SCO).
Online Learning and Solving Infinite Games with an ERM Oracle
In this setting, we provide learning algorithms that only rely on best response oracles and converge to approximate-minimax equilibria in two-player zero-sum games and approximate coarse correlated equilibria in multi-player general-sum games, as long as the game has a bounded fat-threshold dimension.
Adversarially Robust PAC Learnability of Real-Valued Functions
We study robustness to test-time adversarial attacks in the regression setting with $\ell_p$ losses and arbitrary perturbation sets.
Learning Revenue Maximization using Posted Prices for Stochastic Strategic Patient Buyers
Following this, we show how to compute both the optimal pure and mixed strategies.
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability
This shows that there is a significant benefit in semi-supervised robust learning even in the worst-case distribution-free model, and establishes a gap between the supervised and semi-supervised label complexities which is known not to hold in standard non-robust PAC learning.
Fat-Shattering Dimension of $k$-fold Aggregations
We provide estimates on the fat-shattering dimension of aggregation rules of real-valued function classes.
A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators
Classical streaming algorithms operate under the (not always reasonable) assumption that the input stream is fixed in advance.
Domain Invariant Adversarial Learning
We present a new adversarial training method, Domain Invariant Adversarial Learning (DIAL), which learns a feature representation that is both robust and domain invariant.
Prediction with Corrupted Expert Advice
We revisit the fundamental problem of prediction with expert advice, in a setting where the environment is benign and generates losses stochastically, but the feedback observed by the learner is subject to a moderate adversarial corruption.
Improved Generalization Bounds for Adversarially Robust Learning
For binary classification, the algorithm of Feige et al. (2015) uses a regret minimization algorithm and an ERM oracle as a black box; we adapt it for the multiclass and regression settings.
Agnostic Sample Compression Schemes for Regression
For the $\ell_2$ loss, does every function class admit an approximate compression scheme of polynomial size in the fat-shattering dimension?
