Steve Hanneke

Uniform Convergence Beyond Glivenko-Cantelli ALT 2026 Reliable Active Apprenticeship Learning ALT 2025 Sample Compression Scheme Reductions ALT 2025 Data Selection for ERMs COLT 2025 Universal Rates of ERM for Agnostic Learning COLT 2025 A Trichotomy for List Transductive Online Learning ICML 2025 Representation Preserving Multiclass Agnostic to Realizable Reduction ICML 2025 Universal Rates for Multiclass Learning with Bandit Feedback COLT 2025 Private List Learnability vs. Online List Learnability COLT 2025 Open Problem: Data Selection for Regression Tasks COLT 2025 Proofs as Explanations: Short Certificates for Reliable Predictions COLT 2025 For Universal Multiclass Online Learning, Bandit Feedback and Full Supervision are Equivalent ALT 2025 A Complete Characterization of Learnability for Stochastic Noisy Bandits ALT 2025 Universal Rates for Regression: Separations between Cut-Off and Absolute Loss COLT 2024 Improved Sample Complexity for Multiclass PAC Learning NIPS 2024 A Theory of Optimistically Universal Online Learnability for General Concept Classes NIPS 2024 Multiclass Transductive Online Learning NIPS 2024 Universal Rates for Active Learning NIPS 2024 Learning from Snapshots of Discrete and Continuous Data Streams NIPS 2024 Universal Rates of Empirical Risk Minimization NIPS 2024 Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs NIPS 2024 Agnostic Sample Compression Schemes for Regression ICML 2024 The Dimension of Self-Directed Learning ALT 2024 Efficient Agnostic Learning with Average Smoothness ALT 2024 Open problem: Direct Sums in Learning Theory COLT 2024 List Sample Compression and Uniform Convergence COLT 2024 The Star Number and Eluder Dimension: Elementary Observations About the Dimensions of Disagreement COLT 2024 Dual VC Dimension Obstructs Sample Compression by Embeddings COLT 2024 Universal Rates for Multiclass Learning COLT 2023 Bandit Learnability can be Undecidable COLT 2023 Fine-Grained Distribution-Dependent Learning Curves COLT 2023 Improper Multiclass Boosting COLT 2023 Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension COLT 2023 A Trichotomy for Transductive Online Learning NIPS 2023 Limits of Model Selection under Transfer Learning COLT 2023 Adversarially Robust PAC Learnability of Real-Valued Functions ICML 2023 Reliable learning in challenging environments NIPS 2023 Optimal Learners for Realizable Regression: PAC Learning and Online Learning NIPS 2023 Near-optimal learning with average Hölder smoothness NIPS 2023 Multiclass Online Learning and Uniform Convergence COLT 2023 Adversarial Resilience in Sequential Prediction via Abstention NIPS 2023 Universally Consistent Online Learning with Arbitrarily Dependent Responses ALT 2022 A Characterization of Semi-Supervised Adversarially Robust PAC Learnability NIPS 2022 Universal Rates for Interactive Learning NIPS 2022 On Optimal Learning Under Targeted Data Poisoning NIPS 2022 Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization NIPS 2022 Transductive Robust Learning Guarantees AISTATS 2022 Universal Online Learning with Unbounded Losses: Memory Is All You Need ALT 2022 Robustly-reliable learners under poisoning attacks COLT 2022 Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries AISTATS 2021 Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound ALT 2021 Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games COLT 2021 Robust learning under clean-label attack COLT 2021 Adversarially Robust Learning with Unknown Perturbation Sets COLT 2021 Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible? COLT 2021 Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes JMLR 2021 Reducing Adversarially Robust Learning to Non-Robust PAC Learning NIPS 2020 Proper Learning, Helly Number, and an Optimal SVM Bound COLT 2020 On the Value of Target Data in Transfer Learning NIPS 2019 A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes ALT 2019 Sample Compression for Real-Valued Learners ALT 2019 Statistical Learning under Nonstationary Mixing Processes AISTATS 2019 VC Classes are Adversarially Robustly Learnable, but Only Improperly COLT 2019 Actively Avoiding Nonsense in Generative Models COLT 2018 Algorithmic Learning Theory (ALT) 2017: Preface ALT 2017 The Optimal Sample Complexity of PAC Learning JMLR 2016 Refined Error Bounds for Several Learning Algorithms JMLR 2016 A Compression Technique for Analyzing Disagreement-Based Active Learning JMLR 2015 Minimax Analysis of Active Learning JMLR 2015 Activized Learning with Uniform Classification Noise ICML 2013 Robust Interactive Learning COLT 2012 Activized Learning: Transforming Passive to Active with Improved Label Complexity JMLR 2012 Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning COLT 2011 The Sample Complexity of Self-Verifying Bayesian Active Learning AISTATS 2011 Negative Results for Active Learning with Convex Losses AISTATS 2010