 Spherical dimensionAnnual Conference Computational Learning Theory (COLT), 2025 |
 Sample Compression Scheme ReductionsInternational Conference on Algorithmic Learning Theory (ALT), 2024 |
 A Characterization of List RegressionInternational Conference on Algorithmic Learning Theory (ALT), 2024 |
 Sample Compression Unleashed: New Generalization Bounds for Real Valued LossesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024 |
 A Theory of Interpretable ApproximationsAnnual Conference Computational Learning Theory (COLT), 2024 |
 Applications of Littlestone dimension to query learning and to
compressionInternational Symposium on Mathematical Foundations of Computer Science (MFCS), 2023 |
 Multiclass Learnability Does Not Imply Sample CompressionInternational Conference on Algorithmic Learning Theory (ALT), 2023 |
 Private Distribution Learning with Public Data: The View from Sample
CompressionNeural Information Processing Systems (NeurIPS), 2023 |
 Optimal Learners for Realizable Regression: PAC Learning and Online
LearningNeural Information Processing Systems (NeurIPS), 2023 |
 Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and RejectionInternational Conference on Machine Learning (ICML), 2023 |
 Unlabelled Sample Compression Schemes for Intersection-Closed Classes
and Extremal ClassesNeural Information Processing Systems (NeurIPS), 2022 |
 Sample compression schemes for balls in graphsInternational Symposium on Mathematical Foundations of Computer Science (MFCS), 2022 |
 Learning Losses for Strategic ClassificationAAAI Conference on Artificial Intelligence (AAAI), 2022 |
 Adversarially Robust Learning with ToleranceInternational Conference on Algorithmic Learning Theory (ALT), 2022 |
 A Characterization of Semi-Supervised Adversarially-Robust PAC
LearnabilityNeural Information Processing Systems (NeurIPS), 2022 |
 Adaptive Data Analysis with Correlated ObservationsInternational Conference on Machine Learning (ICML), 2022 |
 Learning with distributional invertersInternational Conference on Algorithmic Learning Theory (ALT), 2021 |
 Towards a Unified Information-Theoretic Framework for GeneralizationNeural Information Processing Systems (NeurIPS), 2021 |
 Primal and Dual Combinatorial DimensionsDiscrete Applied Mathematics (DAM), 2021 |
 A Theory of PAC Learnability of Partial Concept ClassesIEEE Annual Symposium on Foundations of Computer Science (FOCS), 2021 |
 Adversarially Robust Learning with Unknown Perturbation SetsAnnual Conference Computational Learning Theory (COLT), 2021 |
 Online Learning with Simple Predictors and a Combinatorial
Characterization of Minimax in 0/1 GamesAnnual Conference Computational Learning Theory (COLT), 2021 |
 The VC-Dimension of Axis-Parallel Boxes on the TorusJournal of Complexity (J. Complexity), 2020 |
 Learning from weakly dependent data under Dobrushin's conditionAnnual Conference Computational Learning Theory (COLT), 2019 |
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 VC Classes are Adversarially Robustly Learnable, but Only ImproperlyAnnual Conference Computational Learning Theory (COLT), 2019 |
 Average-Case Information Complexity of LearningInternational Conference on Algorithmic Learning Theory (ALT), 2018 |
