Hanneke, Steve

95 publications

ALT 2025 A Complete Characterization of Learnability for Stochastic Noisy Bandits Steve Hanneke, Kun Wang
ICML 2025 A Trichotomy for List Transductive Online Learning Steve Hanneke, Amirreza Shaeiri
NeurIPS 2025 Agnostic Active Learning Is Always Better than Passive Learning Steve Hanneke
COLT 2025 Data Selection for ERMs Steve Hanneke, Shay Moran, Alexander Shlimovich, Amir Yehudayoff
ALT 2025 For Universal Multiclass Online Learning, Bandit Feedback and Full Supervision Are Equivalent Steve Hanneke, Amirreza Shaeiri, Hongao Wang
NeurIPS 2025 Marginal-Nonuniform PAC Learnability Steve Hanneke, Shay Moran, Maximilian Thiessen
NeurIPS 2025 Non-Uniform Multiclass Learning with Bandit Feedback Steve Hanneke, Amirreza Shaeiri, Hongao Wang
NeurIPS 2025 On Union-Closedness of Language Generation Steve Hanneke, Amin Karbasi, Anay Mehrotra, Grigoris Velegkas
COLT 2025 Open Problem: Data Selection for Regression Tasks Steve Hanneke, Shay Moran, Alexander Shlimovich, Amir Yehudayoff
NeurIPS 2025 Optimal Mistake Bounds for Transductive Online Learning Zachary Chase, Steve Hanneke, Shay Moran, Jonathan Shafer
COLT 2025 Private List Learnability vs. Online List Learnability Steve Hanneke, Shay Moran, Hilla Schefler, Iska Tsubari
COLT 2025 Proofs as Explanations: Short Certificates for Reliable Predictions Avrim Blum, Steve Hanneke, Chirag Pabbaraju, Donya Saless
ALT 2025 Reliable Active Apprenticeship Learning Steve Hanneke, Liu Yang, Gongju Wang, Yulun Song
ICML 2025 Representation Preserving Multiclass Agnostic to Realizable Reduction Steve Hanneke, Qinglin Meng, Amirreza Shaeiri
ALT 2025 Sample Compression Scheme Reductions Idan Attias, Steve Hanneke, Arvind Ramaswami
NeurIPS 2025 Tradeoffs Between Mistakes and ERM Oracle Calls in Online and Transductive Online Learning Idan Attias, Steve Hanneke, Arvind Ramaswami
COLT 2025 Universal Rates for Multiclass Learning with Bandit Feedback Steve Hanneke, Amirreza Shaeiri, Qian Zhang
COLT 2025 Universal Rates of ERM for Agnostic Learning Steve Hanneke, Mingyue Xu
NeurIPS 2024 A Theory of Optimistically Universal Online Learnability for General Concept Classes Steve Hanneke, Hongao Wang
ICML 2024 Agnostic Sample Compression Schemes for Regression Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
NeurIPS 2024 Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran
COLT 2024 Dual VC Dimension Obstructs Sample Compression by Embeddings Zachary Chase, Bogdan Chornomaz, Steve Hanneke, Shay Moran, Amir Yehudayoff
ALT 2024 Efficient Agnostic Learning with Average Smoothness Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
NeurIPS 2024 Improved Sample Complexity for Multiclass PAC Learning Steve Hanneke, Shay Moran, Qian Zhang
NeurIPS 2024 Learning from Snapshots of Discrete and Continuous Data Streams Pramith Devulapalli, Steve Hanneke
COLT 2024 List Sample Compression and Uniform Convergence Steve Hanneke, Shay Moran, Waknine Tom
NeurIPS 2024 Multiclass Transductive Online Learning Steve Hanneke, Vinod Raman, Amirreza Shaeri, Unqiue Subedi
COLT 2024 Open Problem: Direct Sums in Learning Theory Steve Hanneke, Shay Moran, Waknine Tom
ALT 2024 The Dimension of Self-Directed Learning Pramith Devulapalli, Steve Hanneke
COLT 2024 The Star Number and Eluder Dimension: Elementary Observations About the Dimensions of Disagreement Steve Hanneke
NeurIPS 2024 Universal Rates for Active Learning Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas
COLT 2024 Universal Rates for Regression: Separations Between Cut-Off and Absolute Loss Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas
NeurIPS 2024 Universal Rates of Empirical Risk Minimization Steve Hanneke, Mingyue Xu
NeurIPS 2023 A Trichotomy for Transductive Online Learning Steve Hanneke, Shay Moran, Jonathan Shafer
NeurIPS 2023 Adversarial Resilience in Sequential Prediction via Abstention Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty
ICML 2023 Adversarially Robust PAC Learnability of Real-Valued Functions Idan Attias, Steve Hanneke
COLT 2023 Bandit Learnability Can Be Undecidable Steve Hanneke, Liu Yang
COLT 2023 Fine-Grained Distribution-Dependent Learning Curves Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya Tolstikhin
COLT 2023 Improper Multiclass Boosting Nataly Brukhim, Steve Hanneke, Shay Moran
COLT 2023 Limits of Model Selection Under Transfer Learning Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh
COLT 2023 Multiclass Online Learning and Uniform Convergence Steve Hanneke, Shay Moran, Vinod Raman, Unique Subedi, Ambuj Tewari
NeurIPS 2023 Near-Optimal Learning with Average Hölder Smoothness Guy Kornowski, Steve Hanneke, Aryeh Kontorovich
NeurIPS 2023 Optimal Learners for Realizable Regression: PAC Learning and Online Learning Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas
COLT 2023 Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran
NeurIPS 2023 Reliable Learning in Challenging Environments Maria-Florina F Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma
COLT 2023 Universal Rates for Multiclass Learning Steve Hanneke, Shay Moran, Qian Zhang
AISTATS 2022 Transductive Robust Learning Guarantees Omar Montasser, Steve Hanneke, Nathan Srebro
NeurIPS 2022 A Characterization of Semi-Supervised Adversarially Robust PAC Learnability Idan Attias, Steve Hanneke, Yishay Mansour
NeurIPS 2022 Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization Omar Montasser, Steve Hanneke, Nati Srebro
NeurIPS 2022 On Optimal Learning Under Targeted Data Poisoning Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran
COLT 2022 Robustly-Reliable Learners Under Poisoning Attacks Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma
ALT 2022 Universal Online Learning with Unbounded Losses: Memory Is All You Need Moïse Blanchard, Romain Cosson, Steve Hanneke
NeurIPS 2022 Universal Rates for Interactive Learning Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas
ALT 2022 Universally Consistent Online Learning with Arbitrarily Dependent Responses Steve Hanneke
AISTATS 2021 Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries Steve Hanneke, Liu Yang
COLT 2021 Adversarially Robust Learning with Unknown Perturbation Sets Omar Montasser, Steve Hanneke, Nathan Srebro
JMLR 2021 Learning Whenever Learning Is Possible: Universal Learning Under General Stochastic Processes Steve Hanneke
COLT 2021 Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games Steve Hanneke, Roi Livni, Shay Moran
COLT 2021 Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible? Steve Hanneke
COLT 2021 Robust Learning Under Clean-Label Attack Avrim Blum, Steve Hanneke, Jian Qian, Han Shao
ALT 2021 Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound Steve Hanneke, Aryeh Kontorovich
COLT 2020 Proper Learning, Helly Number, and an Optimal SVM Bound Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy
NeurIPS 2020 Reducing Adversarially Robust Learning to Non-Robust PAC Learning Omar Montasser, Steve Hanneke, Nati Srebro
ALT 2019 A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes Steve Hanneke, Aryeh Kontorovich
NeurIPS 2019 On the Value of Target Data in Transfer Learning Steve Hanneke, Samory Kpotufe
ALT 2019 Sample Compression for Real-Valued Learners Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
AISTATS 2019 Statistical Learning Under Nonstationary Mixing Processes Steve Hanneke, Liu Yang
COLT 2019 VC Classes Are Adversarially Robustly Learnable, but Only Improperly Omar Montasser, Steve Hanneke, Nathan Srebro
COLT 2018 Actively Avoiding Nonsense in Generative Models Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, Christos Tzamos
ALT 2017 Algorithmic Learning Theory (ALT) 2017: Preface Steve Hanneke, Lev Reyzin
ALT 2016 Localization of VC Classes: Beyond Local Rademacher Complexities Nikita Zhivotovskiy, Steve Hanneke
JMLR 2016 Refined Error Bounds for Several Learning Algorithms Steve Hanneke
JMLR 2016 The Optimal Sample Complexity of PAC Learning Steve Hanneke
JMLR 2015 A Compression Technique for Analyzing Disagreement-Based Active Learning Yair Wiener, Steve Hanneke, Ran El-Yaniv
ALT 2015 Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks Liu Yang, Steve Hanneke, Jaime G. Carbonell
ALT 2015 Learning with a Drifting Target Concept Steve Hanneke, Varun Kanade, Liu Yang
JMLR 2015 Minimax Analysis of Active Learning Steve Hanneke, Liu Yang
FnTML 2014 Theory of Disagreement-Based Active Learning Steve Hanneke
MLJ 2013 A Theory of Transfer Learning with Applications to Active Learning Liu Yang, Steve Hanneke, Jaime G. Carbonell
ICML 2013 Activized Learning with Uniform Classification Noise Liu Yang, Steve Hanneke
JMLR 2012 Activized Learning: Transforming Passive to Active with Improved Label Complexity Steve Hanneke
COLT 2012 Robust Interactive Learning Maria Florina Balcan, Steve Hanneke
COLT 2011 Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning Liu Yang, Steve Hanneke, Jaime Carbonell
AISTATS 2011 The Sample Complexity of Self-Verifying Bayesian Active Learning Liu Yang, Steve Hanneke, Jaime Carbonell
ALT 2010 Bayesian Active Learning Using Arbitrary Binary Valued Queries Liu Yang, Steve Hanneke, Jaime G. Carbonell
AISTATS 2010 Negative Results for Active Learning with Convex Losses Steve Hanneke, Liu Yang
MLJ 2010 The True Sample Complexity of Active Learning Maria-Florina Balcan, Steve Hanneke, Jennifer Wortman Vaughan
COLT 2009 Adaptive Rates of Convergence in Active Learning Steve Hanneke
AISTATS 2009 Network Completion and Survey Sampling Steve Hanneke, Eric P. Xing
COLT 2008 The True Sample Complexity of Active Learning Maria-Florina Balcan, Steve Hanneke, Jennifer Wortman
ICML 2007 A Bound on the Label Complexity of Agnostic Active Learning Steve Hanneke
ICML 2007 Recovering Temporally Rewiring Networks: A Model-Based Approach Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing
COLT 2007 Teaching Dimension and the Complexity of Active Learning Steve Hanneke
ICML 2006 An Analysis of Graph Cut Size for Transductive Learning Steve Hanneke
ICML 2006 Discrete Temporal Models of Social Networks Steve Hanneke, Eric P. Xing