An analysis of graph cut size for transductive learning. - Steve Hanneke (ICML, 2006)

Discrete Temporal Models of Social Networks. - Steve Hanneke; Eric P. Xing (SNA@ICML, 2006)

Teaching Dimension and the Complexity of Active Learning. - Steve Hanneke (COLT, 2007)

Recovering temporally rewiring networks: a model-based approach. - Fan Guo 006; Steve Hanneke; Wenjie Fu 002; Eric P. Xing (ICML, 2007)

A bound on the label complexity of agnostic active learning. - Steve Hanneke (ICML, 2007)

Adaptive Rates of Convergence in Active Learning. - Steve Hanneke (COLT, 2009)

Network Completion and Survey Sampling. - Steve Hanneke; Eric P. Xing (AISTATS, 2009)

Bayesian Active Learning Using Arbitrary Binary Valued Queries. - Liu Yang 001; Steve Hanneke; Jaime G. Carbonell (ALT, 2010)

Negative Results for Active Learning with Convex Losses. - Steve Hanneke; Liu Yang 001 (AISTATS, 2010)

The true sample complexity of active learning. - Maria-Florina Balcan; Steve Hanneke; Jennifer Wortman Vaughan (Mach. Learn., 2010)

The Sample Complexity of Self-Verifying Bayesian Active Learning. - Liu Yang 001; Steve Hanneke; Jaime G. Carbonell (AISTATS, 2011)

Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning. - Liu Yang 001; Steve Hanneke; Jaime G. Carbonell (COLT, 2011)

Surrogate Losses in Passive and Active Learning - Steve Hanneke; Liu Yang 001 (CoRR, 2012)

Robust Interactive Learning. - Maria-Florina Balcan; Steve Hanneke (COLT, 2012)

Activized Learning: Transforming Passive to Active with Improved Label Complexity. - Steve Hanneke (J. Mach. Learn. Res., 2012)

Activized Learning with Uniform Classification Noise. - Liu Yang 001; Steve Hanneke (ICML (2), 2013)

A theory of transfer learning with applications to active learning. - Liu Yang 001; Steve Hanneke; Jaime G. Carbonell (Mach. Learn., 2013)

Theory of Disagreement-Based Active Learning. - Steve Hanneke (Found. Trends Mach. Learn., 2014)

Learning with a Drifting Target Concept. - Steve Hanneke; Varun Kanade; Liu Yang 001 (ALT, 2015)

Statistical Learning under Nonstationary Mixing Processes. - Steve Hanneke; Tommi S. Jaakkola; Liu Yang 001 (CoRR, 2015)

Minimax analysis of active learning. - Steve Hanneke; Liu Yang 001 (J. Mach. Learn. Res., 2015)

A compression technique for analyzing disagreement-based active learning. - Yair Wiener; Steve Hanneke; Ran El-Yaniv (J. Mach. Learn. Res., 2015)

The Optimal Sample Complexity of PAC Learning. - Steve Hanneke (J. Mach. Learn. Res., 2016)

Refined Error Bounds for Several Learning Algorithms. - Steve Hanneke (J. Mach. Learn. Res., 2016)

Learning with Changing Features. - Amit Dhurandhar; Steve Hanneke; Liu Yang 001 (CoRR, 2017)

Actively Avoiding Nonsense in Generative Models. - Steve Hanneke; Adam Tauman Kalai; Gautam Kamath 001; Christos Tzamos (COLT, 2018)

A New Lower Bound for Agnostic Learning with Sample Compression Schemes. - Steve Hanneke; Aryeh Kontorovich (CoRR, 2018)

Agnostic Sample Compression for Linear Regression. - Steve Hanneke; Aryeh Kontorovich; Menachem Sadigurschi (CoRR, 2018)

Testing piecewise functions. - Steve Hanneke; Liu Yang 001 (Theor. Comput. Sci., 2018)

Bounds on the minimax rate for estimating a prior over a VC class from independent learning tasks. - Liu Yang 001; Steve Hanneke; Jaime G. Carbonell (Theor. Comput. Sci., 2018)

Localization of VC classes: Beyond local Rademacher complexities. - Nikita Zhivotovskiy; Steve Hanneke (Theor. Comput. Sci., 2018)

Statistical Learning under Nonstationary Mixing Processes. - Steve Hanneke; Liu Yang 001 (AISTATS, 2019)

A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes. - Steve Hanneke; Aryeh Kontorovich (ALT, 2019)

Sample Compression for Real-Valued Learners. - Steve Hanneke; Aryeh Kontorovich; Menachem Sadigurschi (ALT, 2019)

VC Classes are Adversarially Robustly Learnable, but Only Improperly. - Omar Montasser; Steve Hanneke; Nathan Srebro (COLT, 2019)

On the Value of Target Data in Transfer Learning. - Steve Hanneke; Samory Kpotufe (NeurIPS, 2019)

Optimality of SVM: Novel proofs and tighter bounds. - Steve Hanneke; Aryeh Kontorovich (Theor. Comput. Sci., 2019)

Proper Learning, Helly Number, and an Optimal SVM Bound. - Olivier Bousquet; Steve Hanneke; Shay Moran; Nikita Zhivotovskiy (COLT, 2020)

Universal Bayes Consistency in Metric Spaces. - Steve Hanneke; Aryeh Kontorovich; Sivan Sabato; Roi Weiss (ITA, 2020)

Reducing Adversarially Robust Learning to Non-Robust PAC Learning. - Omar Montasser; Steve Hanneke; Nati Srebro (NeurIPS, 2020)

A No-Free-Lunch Theorem for MultiTask Learning. - Steve Hanneke; Samory Kpotufe (CoRR, 2020)

Special issue on ALT 2017: Guest Editors' Introduction. - Steve Hanneke; Lev Reyzin (Theor. Comput. Sci., 2020)

Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries. - Steve Hanneke; Liu Yang 001 (AISTATS, 2021)

Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound. - Steve Hanneke; Aryeh Kontorovich (ALT, 2021)

Robust learning under clean-label attack. - Avrim Blum; Steve Hanneke; Jian Qian; Han Shao (COLT, 2021)

Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible? - 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)

Adversarially Robust Learning with Unknown Perturbation Sets. - Omar Montasser; Steve Hanneke; Nathan Srebro (COLT, 2021)

A Theory of PAC Learnability of Partial Concept Classes. - Noga Alon; Steve Hanneke; Ron Holzman; Shay Moran (FOCS, 2021)

A theory of universal learning. - Olivier Bousquet; Steve Hanneke; Shay Moran; Ramon van Handel; Amir Yehudayoff (STOC, 2021)

Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. - Steve Hanneke (J. Mach. Learn. Res., 2021)

Transductive Robust Learning Guarantees. - Omar Montasser; Steve Hanneke; Nathan Srebro (AISTATS, 2022)

Universal Online Learning with Unbounded Losses: Memory Is All You Need. - Moïse Blanchard; Romain Cosson; Steve Hanneke (ALT, 2022)

Universally Consistent Online Learning with Arbitrarily Dependent Responses. - Steve Hanneke (ALT, 2022)

Robustly-reliable learners under poisoning attacks. - Maria-Florina Balcan; Avrim Blum; Steve Hanneke; Dravyansh Sharma (COLT, 2022)

A Characterization of Semi-Supervised Adversarially Robust PAC Learnability. - Idan Attias; Steve Hanneke; Yishay Mansour (NeurIPS, 2022)

On Optimal Learning Under Targeted Data Poisoning. - Steve Hanneke; Amin Karbasi; Mohammad Mahmoody; Idan Mehalel; Shay Moran (NeurIPS, 2022)

Universal Rates for Interactive Learning. - Steve Hanneke; Amin Karbasi; Shay Moran; Grigoris Velegkas (NeurIPS, 2022)

Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. - Omar Montasser; Steve Hanneke; Nati Srebro (NeurIPS, 2022)

Adversarially Robust Learning of Real-Valued Functions. - Idan Attias; Steve Hanneke (CoRR, 2022)

Fine-Grained Distribution-Dependent Learning Curves. - Olivier Bousquet; Steve Hanneke; Shay Moran; Jonathan Shafer; Ilya O. Tolstikhin (COLT, 2023)

Improper Multiclass Boosting. - Nataly Brukhim; Steve Hanneke; Shay Moran (COLT, 2023)

Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. - Yuval Filmus; Steve Hanneke; Idan Mehalel; Shay Moran (COLT, 2023)

Bandit Learnability can be Undecidable. - Steve Hanneke; Liu Yang 001 (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 (COLT, 2023)

Universal Rates for Multiclass Learning. - Steve Hanneke; Shay Moran; Qian Zhang 067 (COLT, 2023)

Adversarially Robust PAC Learnability of Real-Valued Functions. - Idan Attias; Steve Hanneke (ICML, 2023)

Optimal Learners for Realizable Regression: PAC Learning and Online Learning. - Idan Attias; Steve Hanneke; Alkis Kalavasis; Amin Karbasi; Grigoris Velegkas (NeurIPS, 2023)

Reliable learning in challenging environments. - Maria-Florina Balcan; Steve Hanneke; Rattana Pukdee; Dravyansh Sharma (NeurIPS, 2023)

Adversarial Resilience in Sequential Prediction via Abstention. - Surbhi Goel; Steve Hanneke; Shay Moran; Abhishek Shetty (NeurIPS, 2023)

A Trichotomy for Transductive Online Learning. - Steve Hanneke; Shay Moran; Jonathan Shafer (NeurIPS, 2023)

Near-optimal learning with average Hölder smoothness. - Guy Kornowski; Steve Hanneke; Aryeh Kontorovich (NeurIPS, 2023)

Contextual Bandits and Optimistically Universal Learning. - Moïse Blanchard; Steve Hanneke; Patrick Jaillet (CoRR, 2023)

Non-stationary Contextual Bandits and Universal Learning. - Moïse Blanchard; Steve Hanneke; Patrick Jaillet (CoRR, 2023)

Reliable Learning for Test-time Attacks and Distribution Shift. - Maria-Florina Balcan; Steve Hanneke; Rattana Pukdee; Dravyansh Sharma (CoRR, 2023)

The Dimension of Self-Directed Learning. - Pramith Devulapalli; Steve Hanneke (ALT, 2024)

Efficient Agnostic Learning with Average Smoothness. - Steve Hanneke; Aryeh Kontorovich; Guy Kornowski (ALT, 2024)

Dual VC Dimension Obstructs Sample Compression by Embeddings. - Zachary Chase 001; Bogdan Chornomaz; Steve Hanneke; Shay Moran; Amir Yehudayoff (COLT, 2024)

Universal Rates for Regression: Separations between Cut-Off and Absolute Loss. - Idan Attias; Steve Hanneke; Alkis Kalavasis; Amin Karbasi; Grigoris Velegkas (COLT, 2024)

The Star Number and Eluder Dimension: Elementary Observations About the Dimensions of Disagreement. - Steve Hanneke (COLT, 2024)

List Sample Compression and Uniform Convergence. - Steve Hanneke; Shay Moran; Tom Waknine (COLT, 2024)

Open problem: Direct Sums in Learning Theory. - Steve Hanneke; Shay Moran; Tom Waknine (COLT, 2024)

Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem. - Simone Fioravanti; Steve Hanneke; Shay Moran; Hilla Schefler; Iska Tsubari (FOCS, 2024)

Revisiting Agnostic PAC Learning. - Steve Hanneke; Kasper Green Larsen; Nikita Zhivotovskiy (FOCS, 2024)

Agnostic Sample Compression Schemes for Regression. - Idan Attias; Steve Hanneke; Aryeh Kontorovich; Menachem Sadigurschi (ICML, 2024)

Learning from Snapshots of Discrete and Continuous Data Streams. - Pramith Devulapalli; Steve Hanneke (NeurIPS, 2024)

Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs. - Yuval Filmus; Steve Hanneke; Idan Mehalel; Shay Moran (NeurIPS, 2024)

Universal Rates for Active Learning. - Steve Hanneke; Amin Karbasi; Shay Moran; Grigoris Velegkas (NeurIPS, 2024)

Improved Sample Complexity for Multiclass PAC Learning. - Steve Hanneke; Shay Moran; Qian Zhang 067 (NeurIPS, 2024)

Multiclass Transductive Online Learning. - Steve Hanneke; Vinod Raman; Amirreza Shaeiri; Unique Subedi (NeurIPS, 2024)

A Theory of Optimistically Universal Online Learnability for General Concept Classes. - Steve Hanneke; Hongao Wang (NeurIPS, 2024)

Universal Rates of Empirical Risk Minimization. - Steve Hanneke; Mingyue Xu (NeurIPS, 2024)

A More Unified Theory of Transfer Learning. - Steve Hanneke; Samory Kpotufe (CoRR, 2024)

On the ERM Principle in Meta-Learning. - Yannay Alon; Steve Hanneke; Shay Moran; Uri Shalit (CoRR, 2024)

Sample Compression Scheme Reductions. - Idan Attias; Steve Hanneke; Arvind Ramaswami (ALT, 2025)

Reliable Active Apprenticeship Learning. - Steve Hanneke; Liu Yang 001; Gongju Wang; Yulun Song (ALT, 2025)

For Universal Multiclass Online Learning, Bandit Feedback and Full Supervision are Equivalent. - Steve Hanneke; Amirreza Shaeiri; Hongao Wang (ALT, 2025)

A Complete Characterization of Learnability for Stochastic Noisy Bandits. - Steve Hanneke; Kun Wang (ALT, 2025)

Proofs as Explanations: Short Certificates for Reliable Predictions. - Avrim Blum; Steve Hanneke; Chirag Pabbaraju; Donya Saless (COLT, 2025)

Private List Learnability vs. Online List Learnability. - Steve Hanneke; Shay Moran; Hilla Schefler; Iska Tsubari (COLT, 2025)

Data Selection for ERMs. - Steve Hanneke; Shay Moran; Alexander Shlimovich; Amir Yehudayoff (COLT, 2025)

Open Problem: Data Selection for Regression Tasks. - Steve Hanneke; Shay Moran; Alexander Shlimovich; Amir Yehudayoff (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 (COLT, 2025)

Tradeoffs between Mistakes and ERM Oracle Calls in Online and Transductive Online Learning. - Idan Attias; Steve Hanneke; Arvind Ramaswami (CoRR, 2025)

On Union-Closedness of Language Generation. - Steve Hanneke; Amin Karbasi; Anay Mehrotra; Grigoris Velegkas (CoRR, 2025)