Steve Hanneke - RankMe

176 papers A* 60A 6B 16Misc 2Journal 90Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Theory of Universal Agnostic Learning. CoRR Steve Hanneke, Shay Moran
2026	J	`jnl`	When More Data Doesn't Help: Limits of Adaptation in Multitask Learning. CoRR Steve Hanneke, Mingyue Xu
2025	B	`conf`	A Complete Characterization of Learnability for Stochastic Noisy Bandits. ALT Steve Hanneke, Kun Wang
2025	J	`jnl`	A Theory of Optimistically Universal Online Learnability for General Concept Classes. CoRR Steve Hanneke, Hongao Wang
2025	A*	`conf`	A Trichotomy for List Transductive Online Learning. ICML Steve Hanneke, Amirreza Shaeiri
2025	A*	`conf`	Data Selection for ERMs. COLT Steve Hanneke, Shay Moran, Alexander Shlimovich, Amir Yehudayoff
2025	J	`jnl`	Data Selection for ERMs. CoRR Steve Hanneke, Shay Moran, Alexander Shlimovich, Amir Yehudayoff
2025	B	`conf`	For Universal Multiclass Online Learning, Bandit Feedback and Full Supervision are Equivalent. ALT Steve Hanneke, Amirreza Shaeiri, Hongao Wang
2025	J	`jnl`	On Union-Closedness of Language Generation. CoRR Steve Hanneke, Amin Karbasi, Anay Mehrotra, Grigoris Velegkas
2025	A*	`conf`	Open Problem: Data Selection for Regression Tasks. COLT Steve Hanneke, Shay Moran, Alexander Shlimovich, Amir Yehudayoff
2025	J	`jnl`	Optimal Mistake Bounds for Transductive Online Learning. CoRR Zachary Chase, Steve Hanneke, Shay Moran, Jonathan Shafer
2025	J	`jnl`	Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. SIAM J. Comput. Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran
2025	A*	`conf`	Private List Learnability vs. Online List Learnability. COLT Steve Hanneke, Shay Moran, Hilla Schefler, Iska Tsubari
2025	J	`jnl`	Private List Learnability vs. Online List Learnability. CoRR Steve Hanneke, Shay Moran, Hilla Schefler, Iska Tsubari
2025	A*	`conf`	Proofs as Explanations: Short Certificates for Reliable Predictions. COLT Avrim Blum, Steve Hanneke, Chirag Pabbaraju, Donya Saless
2025	J	`jnl`	Proofs as Explanations: Short Certificates for Reliable Predictions. CoRR Avrim Blum, Steve Hanneke, Chirag Pabbaraju, Donya Saless
2025	B	`conf`	Reliable Active Apprenticeship Learning. ALT Steve Hanneke, Liu Yang, Gongju Wang, Yulun Song
2025	A*	`conf`	Representation Preserving Multiclass Agnostic to Realizable Reduction. ICML Steve Hanneke, Qinglin Meng, Amirreza Shaeiri
2025	J	`jnl`	Sample Complexity of Agnostic Multiclass Classification: Natarajan Dimension Strikes Back. CoRR Alon Cohen, Liad Erez, Steve Hanneke, Tomer Koren, Yishay Mansour, Shay Moran, Qian Zhang
2025	B	`conf`	Sample Compression Scheme Reductions. ALT Idan Attias, Steve Hanneke, Arvind Ramaswami
2025	J	`jnl`	Tradeoffs between Mistakes and ERM Oracle Calls in Online and Transductive Online Learning. CoRR Idan Attias, Steve Hanneke, Arvind Ramaswami
2025	J	`jnl`	Uniform Convergence Beyond Glivenko-Cantelli. CoRR Tanmay Devale, Pramith Devulapalli, Steve Hanneke
2025	A*	`conf`	Universal Rates for Multiclass Learning with Bandit Feedback. COLT Steve Hanneke, Amirreza Shaeiri, Qian Zhang
2025	A*	`conf`	Universal Rates of ERM for Agnostic Learning. COLT Steve Hanneke, Mingyue Xu
2025	J	`jnl`	Universal Rates of ERM for Agnostic Learning. CoRR Steve Hanneke, Mingyue Xu
2024	J	`jnl`	A Complete Characterization of Learnability for Stochastic Noisy Bandits. CoRR Steve Hanneke, Kun Wang
2024	J	`jnl`	A More Unified Theory of Transfer Learning. CoRR Steve Hanneke, Samory Kpotufe
2024	A*	`conf`	A Theory of Optimistically Universal Online Learnability for General Concept Classes. NeurIPS Steve Hanneke, Hongao Wang
2024	A*	`conf`	Agnostic Sample Compression Schemes for Regression. ICML Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
2024	A*	`conf`	Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs. NeurIPS Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran
2024	J	`jnl`	Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs. CoRR Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran
2024	A*	`conf`	Dual VC Dimension Obstructs Sample Compression by Embeddings. COLT Zachary Chase, Bogdan Chornomaz, Steve Hanneke, Shay Moran, Amir Yehudayoff
2024	J	`jnl`	Dual VC Dimension Obstructs Sample Compression by Embeddings. CoRR Zachary Chase, Bogdan Chornomaz, Steve Hanneke, Shay Moran, Amir Yehudayoff
2024	B	`conf`	Efficient Agnostic Learning with Average Smoothness. ALT Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
2024	A*	`conf`	Improved Sample Complexity for Multiclass PAC Learning. NeurIPS Steve Hanneke, Shay Moran, Qian Zhang
2024	A*	`conf`	Learning from Snapshots of Discrete and Continuous Data Streams. NeurIPS Pramith Devulapalli, Steve Hanneke
2024	J	`jnl`	Learning from Snapshots of Discrete and Continuous Data Streams. CoRR Pramith Devulapalli, Steve Hanneke
2024	A*	`conf`	List Sample Compression and Uniform Convergence. COLT Steve Hanneke, Shay Moran, Tom Waknine
2024	J	`jnl`	List Sample Compression and Uniform Convergence. CoRR Steve Hanneke, Shay Moran, Tom Waknine
2024	A*	`conf`	Multiclass Transductive Online Learning. NeurIPS Steve Hanneke, Vinod Raman, Amirreza Shaeiri, Unique Subedi
2024	J	`jnl`	Multiclass Transductive Online Learning. CoRR Steve Hanneke, Vinod Raman, Amirreza Shaeiri, Unique Subedi
2024	J	`jnl`	On the ERM Principle in Meta-Learning. CoRR Yannay Alon, Steve Hanneke, Shay Moran, Uri Shalit
2024	A*	`conf`	Open problem: Direct Sums in Learning Theory. COLT Steve Hanneke, Shay Moran, Tom Waknine
2024	A*	`conf`	Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem. FOCS Simone Fioravanti, Steve Hanneke, Shay Moran, Hilla Schefler, Iska Tsubari
2024	J	`jnl`	Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem. CoRR Simone Fioravanti, Steve Hanneke, Shay Moran, Hilla Schefler, Iska Tsubari
2024	A*	`conf`	Revisiting Agnostic PAC Learning. FOCS Steve Hanneke, Kasper Green Larsen, Nikita Zhivotovskiy
2024	J	`jnl`	Revisiting Agnostic PAC Learning. CoRR Steve Hanneke, Kasper Green Larsen, Nikita Zhivotovskiy
2024	J	`jnl`	Sample Compression Scheme Reductions. CoRR Idan Attias, Steve Hanneke, Arvind Ramaswami
2024	B	`conf`	The Dimension of Self-Directed Learning. ALT Pramith Devulapalli, Steve Hanneke
2024	J	`jnl`	The Dimension of Self-Directed Learning. CoRR Pramith Devulapalli, Steve Hanneke
2024	A*	`conf`	The Star Number and Eluder Dimension: Elementary Observations About the Dimensions of Disagreement. COLT Steve Hanneke
2024	A*	`conf`	Universal Rates for Active Learning. NeurIPS Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas
2024	A*	`conf`	Universal Rates for Regression: Separations between Cut-Off and Absolute Loss. COLT Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas
2024	A*	`conf`	Universal Rates of Empirical Risk Minimization. NeurIPS Steve Hanneke, Mingyue Xu
2024	J	`jnl`	Universal Rates of Empirical Risk Minimization. CoRR Steve Hanneke, Mingyue Xu
2023	A*	`conf`	A Trichotomy for Transductive Online Learning. NeurIPS Steve Hanneke, Shay Moran, Jonathan Shafer
2023	J	`jnl`	A Trichotomy for Transductive Online Learning. CoRR Steve Hanneke, Shay Moran, Jonathan Shafer
2023	A*	`conf`	Adversarial Resilience in Sequential Prediction via Abstention. NeurIPS Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty
2023	J	`jnl`	Adversarial Resilience in Sequential Prediction via Abstention. CoRR Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty
2023	A*	`conf`	Adversarially Robust PAC Learnability of Real-Valued Functions. ICML Idan Attias, Steve Hanneke
2023	A*	`conf`	Bandit Learnability can be Undecidable. COLT Steve Hanneke, Liu Yang
2023	J	`jnl`	Contextual Bandits and Optimistically Universal Learning. CoRR Moïse Blanchard, Steve Hanneke, Patrick Jaillet
2023	J	`jnl`	Efficient Agnostic Learning with Average Smoothness. CoRR Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
2023	A*	`conf`	Fine-Grained Distribution-Dependent Learning Curves. COLT Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin
2023	A*	`conf`	Improper Multiclass Boosting. COLT Nataly Brukhim, Steve Hanneke, Shay Moran
2023	A*	`conf`	Limits of Model Selection under Transfer Learning. COLT Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh
2023	J	`jnl`	Limits of Model Selection under Transfer Learning. CoRR Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh
2023	A*	`conf`	Multiclass Online Learning and Uniform Convergence. COLT Steve Hanneke, Shay Moran, Vinod Raman, Unique Subedi, Ambuj Tewari
2023	A*	`conf`	Near-optimal learning with average Hölder smoothness. NeurIPS Guy Kornowski, Steve Hanneke, Aryeh Kontorovich
2023	J	`jnl`	Near-optimal learning with average Hölder smoothness. CoRR Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
2023	J	`jnl`	Non-stationary Contextual Bandits and Universal Learning. CoRR Moïse Blanchard, Steve Hanneke, Patrick Jaillet
2023	A*	`conf`	Optimal Learners for Realizable Regression: PAC Learning and Online Learning. NeurIPS Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas
2023	J	`jnl`	Optimal Learners for Realizable Regression: PAC Learning and Online Learning. CoRR Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas
2023	A*	`conf`	Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. COLT Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran
2023	J	`jnl`	Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. CoRR Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran
2023	J	`jnl`	Reliable Learning for Test-time Attacks and Distribution Shift. CoRR Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma
2023	A*	`conf`	Reliable learning in challenging environments. NeurIPS Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma
2023	A*	`conf`	Universal Rates for Multiclass Learning. COLT Steve Hanneke, Shay Moran, Qian Zhang
2023	J	`jnl`	Universal Rates for Multiclass Learning. CoRR Steve Hanneke, Shay Moran, Qian Zhang
2022	A*	`conf`	A Characterization of Semi-Supervised Adversarially Robust PAC Learnability. NeurIPS Idan Attias, Steve Hanneke, Yishay Mansour
2022	J	`jnl`	A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability. CoRR Idan Attias, Steve Hanneke, Yishay Mansour
2022	J	`jnl`	Adversarially Robust Learning of Real-Valued Functions. CoRR Idan Attias, Steve Hanneke
2022	A*	`conf`	Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. NeurIPS Omar Montasser, Steve Hanneke, Nati Srebro
2022	J	`jnl`	Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2022	J	`jnl`	Fine-Grained Distribution-Dependent Learning Curves. CoRR Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin
2022	A*	`conf`	On Optimal Learning Under Targeted Data Poisoning. NeurIPS Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran
2022	J	`jnl`	On Optimal Learning Under Targeted Data Poisoning. CoRR Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran
2022	A*	`conf`	Robustly-reliable learners under poisoning attacks. COLT Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma
2022	J	`jnl`	Robustly-reliable learners under poisoning attacks. CoRR Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma
2022	A	`conf`	Transductive Robust Learning Guarantees. AISTATS Omar Montasser, Steve Hanneke, Nathan Srebro
2022	B	`conf`	Universal Online Learning with Unbounded Losses: Memory Is All You Need. ALT Moïse Blanchard, Romain Cosson, Steve Hanneke
2022	J	`jnl`	Universal Online Learning with Unbounded Losses: Memory Is All You Need. CoRR Moïse Blanchard, Romain Cosson, Steve Hanneke
2022	A*	`conf`	Universal Rates for Interactive Learning. NeurIPS Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas
2022	B	`conf`	Universally Consistent Online Learning with Arbitrarily Dependent Responses. ALT Steve Hanneke
2022	J	`jnl`	Universally Consistent Online Learning with Arbitrarily Dependent Responses. CoRR Steve Hanneke
2021	A*	`conf`	A Theory of PAC Learnability of Partial Concept Classes. FOCS Noga Alon, Steve Hanneke, Ron Holzman, Shay Moran
2021	J	`jnl`	A Theory of PAC Learnability of Partial Concept Classes. CoRR Noga Alon, Steve Hanneke, Ron Holzman, Shay Moran
2021	A*	`conf`	A theory of universal learning. STOC Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff
2021	A*	`conf`	Adversarially Robust Learning with Unknown Perturbation Sets. COLT Omar Montasser, Steve Hanneke, Nathan Srebro
2021	J	`jnl`	Adversarially Robust Learning with Unknown Perturbation Sets. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2021	J	`jnl`	Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. J. Mach. Learn. Res. Steve Hanneke
2021	A*	`conf`	Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. COLT Steve Hanneke, Roi Livni, Shay Moran
2021	J	`jnl`	Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. CoRR Steve Hanneke, Roi Livni, Shay Moran
2021	A*	`conf`	Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible? COLT Steve Hanneke
2021	J	`jnl`	Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible? CoRR Steve Hanneke
2021	A*	`conf`	Robust learning under clean-label attack. COLT Avrim Blum, Steve Hanneke, Jian Qian, Han Shao
2021	J	`jnl`	Robust learning under clean-label attack. CoRR Avrim Blum, Steve Hanneke, Jian Qian, Han Shao
2021	B	`conf`	Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound. ALT Steve Hanneke, Aryeh Kontorovich
2021	A	`conf`	Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries. AISTATS Steve Hanneke, Liu Yang
2021	J	`jnl`	Transductive Robust Learning Guarantees. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2020	J	`jnl`	A No-Free-Lunch Theorem for MultiTask Learning. CoRR Steve Hanneke, Samory Kpotufe
2020	J	`jnl`	A Theory of Universal Learning. CoRR Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff
2020	Misc	`conf`	Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. ITA Steve Hanneke
2020	J	`jnl`	On the Value of Target Data in Transfer Learning. CoRR Steve Hanneke, Samory Kpotufe
2020	A*	`conf`	Proper Learning, Helly Number, and an Optimal SVM Bound. COLT Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy
2020	J	`jnl`	Proper Learning, Helly Number, and an Optimal SVM Bound. CoRR Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy
2020	A*	`conf`	Reducing Adversarially Robust Learning to Non-Robust PAC Learning. NeurIPS Omar Montasser, Steve Hanneke, Nati Srebro
2020	J	`jnl`	Reducing Adversarially Robust Learning to Non-Robust PAC Learning. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2020	J	`jnl`	Special issue on ALT 2017: Guest Editors' Introduction. Theor. Comput. Sci. Steve Hanneke, Lev Reyzin
2020	J	`jnl`	Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound. CoRR Steve Hanneke, Aryeh Kontorovich
2020	Misc	`conf`	Universal Bayes Consistency in Metric Spaces. ITA Steve Hanneke, Aryeh Kontorovich, Sivan Sabato, Roi Weiss
2019	B	`conf`	A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes. ALT Steve Hanneke, Aryeh Kontorovich
2019	A*	`conf`	On the Value of Target Data in Transfer Learning. NeurIPS Steve Hanneke, Samory Kpotufe
2019	J	`jnl`	Optimality of SVM: Novel proofs and tighter bounds. Theor. Comput. Sci. Steve Hanneke, Aryeh Kontorovich
2019	B	`conf`	Sample Compression for Real-Valued Learners. ALT Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
2019	A	`conf`	Statistical Learning under Nonstationary Mixing Processes. AISTATS Steve Hanneke, Liu Yang
2019	J	`jnl`	Universal Bayes consistency in metric spaces. CoRR Steve Hanneke, Aryeh Kontorovich, Sivan Sabato, Roi Weiss
2019	A*	`conf`	VC Classes are Adversarially Robustly Learnable, but Only Improperly. COLT Omar Montasser, Steve Hanneke, Nathan Srebro
2019	J	`jnl`	VC Classes are Adversarially Robustly Learnable, but Only Improperly. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2018	J	`jnl`	A New Lower Bound for Agnostic Learning with Sample Compression Schemes. CoRR Steve Hanneke, Aryeh Kontorovich
2018	A*	`conf`	Actively Avoiding Nonsense in Generative Models. COLT Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, Christos Tzamos
2018	J	`jnl`	Actively Avoiding Nonsense in Generative Models. CoRR Steve Hanneke, Adam Kalai, Gautam Kamath, Christos Tzamos
2018	J	`jnl`	Agnostic Sample Compression for Linear Regression. CoRR Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
2018	J	`jnl`	Bounds on the minimax rate for estimating a prior over a VC class from independent learning tasks. Theor. Comput. Sci. Liu Yang, Steve Hanneke, Jaime G. Carbonell
2018	J	`jnl`	Localization of VC classes: Beyond local Rademacher complexities. Theor. Comput. Sci. Nikita Zhivotovskiy, Steve Hanneke
2018	J	`jnl`	Sample Compression for Real-Valued Learners. CoRR Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
2018	J	`jnl`	Testing piecewise functions. Theor. Comput. Sci. Steve Hanneke, Liu Yang
2017	B	`ed.`	International Conference on Algorithmic Learning Theory, ALT 2017, 15-17 October 2017, Kyoto University, Kyoto, Japan ALT Steve Hanneke, Lev Reyzin
2017	J	`jnl`	Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. CoRR Steve Hanneke
2017	J	`jnl`	Learning with Changing Features. CoRR Amit Dhurandhar, Steve Hanneke, Liu Yang
2017	J	`jnl`	Testing Piecewise Functions. CoRR Steve Hanneke, Liu Yang
2016	B	`conf`	Localization of VC Classes: Beyond Local Rademacher Complexities. ALT Nikita Zhivotovskiy, Steve Hanneke
2016	J	`jnl`	Refined Error Bounds for Several Learning Algorithms. J. Mach. Learn. Res. Steve Hanneke
2016	J	`jnl`	The Optimal Sample Complexity of PAC Learning. J. Mach. Learn. Res. Steve Hanneke
2015	J	`jnl`	A compression technique for analyzing disagreement-based active learning. J. Mach. Learn. Res. Yair Wiener, Steve Hanneke, Ran El-Yaniv
2015	B	`conf`	Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. ALT Liu Yang, Steve Hanneke, Jaime G. Carbonell
2015	J	`jnl`	Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. CoRR Liu Yang, Steve Hanneke, Jaime G. Carbonell
2015	B	`conf`	Learning with a Drifting Target Concept. ALT Steve Hanneke, Varun Kanade, Liu Yang
2015	J	`jnl`	Learning with a Drifting Target Concept. CoRR Steve Hanneke, Varun Kanade, Liu Yang
2015	J	`jnl`	Minimax analysis of active learning. J. Mach. Learn. Res. Steve Hanneke, Liu Yang
2015	J	`jnl`	Refined Error Bounds for Several Learning Algorithms. CoRR Steve Hanneke
2015	J	`jnl`	Statistical Learning under Nonstationary Mixing Processes. CoRR Steve Hanneke, Tommi S. Jaakkola, Liu Yang
2015	J	`jnl`	The Optimal Sample Complexity of PAC Learning. CoRR Steve Hanneke
2014	J	`jnl`	A Compression Technique for Analyzing Disagreement-Based Active Learning. CoRR Yair Wiener, Steve Hanneke, Ran El-Yaniv
2014	J	`jnl`	Minimax Analysis of Active Learning. CoRR Steve Hanneke, Liu Yang
2014	J	`jnl`	Theory of Disagreement-Based Active Learning. Found. Trends Mach. Learn. Steve Hanneke
2013	J	`jnl`	A theory of transfer learning with applications to active learning. Mach. Learn. Liu Yang, Steve Hanneke, Jaime G. Carbonell
2013	—	`conf`	Activized Learning with Uniform Classification Noise. ICML (2) Liu Yang, Steve Hanneke
2012	J	`jnl`	Activized Learning: Transforming Passive to Active with Improved Label Complexity. J. Mach. Learn. Res. Steve Hanneke
2012	A*	`conf`	Robust Interactive Learning. COLT Maria-Florina Balcan, Steve Hanneke
2012	J	`jnl`	Surrogate Losses in Passive and Active Learning CoRR Steve Hanneke, Liu Yang
2011	J	`jnl`	Activized Learning: Transforming Passive to Active with Improved Label Complexity CoRR Steve Hanneke
2011	A*	`conf`	Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning. COLT Liu Yang, Steve Hanneke, Jaime G. Carbonell
2011	J	`jnl`	Robust Interactive Learning CoRR Maria-Florina Balcan, Steve Hanneke
2011	A	`conf`	The Sample Complexity of Self-Verifying Bayesian Active Learning. AISTATS Liu Yang, Steve Hanneke, Jaime G. Carbonell
2010	B	`conf`	Bayesian Active Learning Using Arbitrary Binary Valued Queries. ALT Liu Yang, Steve Hanneke, Jaime G. Carbonell
2010	A	`conf`	Negative Results for Active Learning with Convex Losses. AISTATS Steve Hanneke, Liu Yang
2010	J	`jnl`	The true sample complexity of active learning. Mach. Learn. Maria-Florina Balcan, Steve Hanneke, Jennifer Wortman Vaughan
2009	A*	`conf`	Adaptive Rates of Convergence in Active Learning. COLT Steve Hanneke
2009	A	`conf`	Network Completion and Survey Sampling. AISTATS Steve Hanneke, Eric P. Xing
2008	A*	`conf`	The True Sample Complexity of Active Learning. COLT Maria-Florina Balcan, Steve Hanneke, Jennifer Wortman
2007	A*	`conf`	A bound on the label complexity of agnostic active learning. ICML Steve Hanneke
2007	A*	`conf`	Recovering temporally rewiring networks: a model-based approach. ICML Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing
2007	A*	`conf`	Teaching Dimension and the Complexity of Active Learning. COLT Steve Hanneke
2006	A*	`conf`	An analysis of graph cut size for transductive learning. ICML Steve Hanneke
2006	—	`conf`	Discrete Temporal Models of Social Networks. SNA@ICML Steve Hanneke, Eric P. Xing