Steve Hanneke

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 102

Top Venues

CoRR

COLT

NeurIPS

ALT

Publications

Pramith Devulapalli, Steve Hanneke
The Dimension of Self-Directed Learning. CoRR (2024)
Zachary Chase, Bogdan Chornomaz, Steve Hanneke, Shay Moran, Amir Yehudayoff
Dual VC Dimension Obstructs Sample Compression by Embeddings. CoRR (2024)
Simone Fioravanti, Steve Hanneke, Shay Moran, Hilla Schefler, Iska Tsubari
Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem. CoRR (2024)
Steve Hanneke, Shay Moran, Tom Waknine
Open problem: Direct Sums in Learning Theory. COLT (2024)
Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
Efficient Agnostic Learning with Average Smoothness. ALT (2024)
Steve Hanneke
The Star Number and Eluder Dimension: Elementary Observations About the Dimensions of Disagreement. COLT (2024)
Steve Hanneke, Kasper Green Larsen, Nikita Zhivotovskiy
Revisiting Agnostic PAC Learning. CoRR (2024)
Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas
Universal Rates for Regression: Separations between Cut-Off and Absolute Loss. COLT (2024)
Steve Hanneke, Shay Moran, Tom Waknine
List Sample Compression and Uniform Convergence. COLT (2024)
Pramith Devulapalli, Steve Hanneke
The Dimension of Self-Directed Learning. ALT (2024)
Steve Hanneke, Shay Moran, Tom Waknine
List Sample Compression and Uniform Convergence. CoRR (2024)
Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran
Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs. CoRR (2024)
Zachary Chase, Bogdan Chornomaz, Steve Hanneke, Shay Moran, Amir Yehudayoff
Dual VC Dimension Obstructs Sample Compression by Embeddings. COLT (2024)
Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin
Fine-Grained Distribution-Dependent Learning Curves. COLT (2023)
Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
Efficient Agnostic Learning with Average Smoothness. CoRR (2023)
Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. COLT (2023)
Steve Hanneke, Shay Moran, Jonathan Shafer
A Trichotomy for Transductive Online Learning. NeurIPS (2023)
Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas
Optimal Learners for Realizable Regression: PAC Learning and Online Learning. NeurIPS (2023)
Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty
Adversarial Resilience in Sequential Prediction via Abstention. CoRR (2023)
Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty
Adversarial Resilience in Sequential Prediction via Abstention. NeurIPS (2023)
Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma
Reliable Learning for Test-time Attacks and Distribution Shift. CoRR (2023)
Moïse Blanchard, Steve Hanneke, Patrick Jaillet
Contextual Bandits and Optimistically Universal Learning. CoRR (2023)
Nataly Brukhim, Steve Hanneke, Shay Moran
Improper Multiclass Boosting. COLT (2023)
Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh
Limits of Model Selection under Transfer Learning. COLT (2023)
Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
Near-optimal learning with average Hölder smoothness. CoRR (2023)
Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma
Reliable learning in challenging environments. NeurIPS (2023)
Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh
Limits of Model Selection under Transfer Learning. CoRR (2023)
Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. CoRR (2023)
Idan Attias, Steve Hanneke
Adversarially Robust PAC Learnability of Real-Valued Functions. ICML (2023)
Steve Hanneke, Shay Moran, Qian Zhang
Universal Rates for Multiclass Learning. CoRR (2023)
Steve Hanneke, Shay Moran, Vinod Raman, Unique Subedi, Ambuj Tewari
Multiclass Online Learning and Uniform Convergence. COLT (2023)
Steve Hanneke, Shay Moran, Qian Zhang
Universal Rates for Multiclass Learning. COLT (2023)
Guy Kornowski, Steve Hanneke, Aryeh Kontorovich
Near-optimal learning with average Hölder smoothness. NeurIPS (2023)
Moïse Blanchard, Steve Hanneke, Patrick Jaillet
Non-stationary Contextual Bandits and Universal Learning. CoRR (2023)
Steve Hanneke, Shay Moran, Jonathan Shafer
A Trichotomy for Transductive Online Learning. CoRR (2023)
Steve Hanneke, Liu Yang
Bandit Learnability can be Undecidable. COLT (2023)
Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas
Optimal Learners for Realizable Regression: PAC Learning and Online Learning. CoRR (2023)
Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran
On Optimal Learning Under Targeted Data Poisoning. NeurIPS (2022)
Omar Montasser, Steve Hanneke, Nati Srebro
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. NeurIPS (2022)
Omar Montasser, Steve Hanneke, Nathan Srebro
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. CoRR (2022)
Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran
On Optimal Learning Under Targeted Data Poisoning. CoRR (2022)
Idan Attias, Steve Hanneke
Adversarially Robust Learning of Real-Valued Functions. CoRR (2022)
Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas
Universal Rates for Interactive Learning. NeurIPS (2022)
Noga Alon, Steve Hanneke, Ron Holzman, Shay Moran
A Theory of PAC Learnability of Partial Concept Classes. FOCS (2022)
Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma
Robustly-reliable learners under poisoning attacks. CoRR (2022)
Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin
Fine-Grained Distribution-Dependent Learning Curves. CoRR (2022)
Idan Attias, Steve Hanneke, Yishay Mansour
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability. CoRR (2022)
Moïse Blanchard, Romain Cosson, Steve Hanneke
Universal Online Learning with Unbounded Losses: Memory Is All You Need. ALT (2022)
Steve Hanneke
Universally Consistent Online Learning with Arbitrarily Dependent Responses. ALT (2022)
Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma
Robustly-reliable learners under poisoning attacks. COLT (2022)
Idan Attias, Steve Hanneke, Yishay Mansour
A Characterization of Semi-Supervised Adversarially Robust PAC Learnability. NeurIPS (2022)
Steve Hanneke
Universally Consistent Online Learning with Arbitrarily Dependent Responses. CoRR (2022)
Moïse Blanchard, Romain Cosson, Steve Hanneke
Universal Online Learning with Unbounded Losses: Memory Is All You Need. CoRR (2022)
Omar Montasser, Steve Hanneke, Nathan Srebro
Transductive Robust Learning Guarantees. AISTATS (2022)
Steve Hanneke, Roi Livni, Shay Moran
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. CoRR (2021)
Omar Montasser, Steve Hanneke, Nathan Srebro
Adversarially Robust Learning with Unknown Perturbation Sets. CoRR (2021)
Steve Hanneke
Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. J. Mach. Learn. Res. 22 (2021)
Omar Montasser, Steve Hanneke, Nathan Srebro
Adversarially Robust Learning with Unknown Perturbation Sets. COLT (2021)
Omar Montasser, Steve Hanneke, Nathan Srebro
Transductive Robust Learning Guarantees. CoRR (2021)
Steve Hanneke, Liu Yang
Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries. AISTATS (2021)
Noga Alon, Steve Hanneke, Ron Holzman, Shay Moran
A Theory of PAC Learnability of Partial Concept Classes. CoRR (2021)
Steve Hanneke
Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible? CoRR (2021)
Avrim Blum, Steve Hanneke, Jian Qian, Han Shao
Robust learning under clean-label attack. COLT (2021)
Steve Hanneke, Aryeh Kontorovich
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound. ALT (2021)
Steve Hanneke, Roi Livni, Shay Moran
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. COLT (2021)
Steve Hanneke
Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible? COLT (2021)
Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff
A theory of universal learning. STOC (2021)
Noga Alon, Steve Hanneke, Ron Holzman, Shay Moran
A Theory of PAC Learnability of Partial Concept Classes. FOCS (2021)
Avrim Blum, Steve Hanneke, Jian Qian, Han Shao
Robust learning under clean-label attack. CoRR (2021)
Steve Hanneke, Aryeh Kontorovich
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound. CoRR (2020)
Steve Hanneke, Samory Kpotufe
On the Value of Target Data in Transfer Learning. CoRR (2020)
Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff
A Theory of Universal Learning. CoRR (2020)
Steve Hanneke
Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. ITA (2020)
Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy
Proper Learning, Helly Number, and an Optimal SVM Bound. COLT (2020)
Steve Hanneke, Lev Reyzin
Special issue on ALT 2017: Guest Editors' Introduction. Theor. Comput. Sci. 808 (2020)
Omar Montasser, Steve Hanneke, Nati Srebro
Reducing Adversarially Robust Learning to Non-Robust PAC Learning. NeurIPS (2020)
Omar Montasser, Steve Hanneke, Nathan Srebro
Reducing Adversarially Robust Learning to Non-Robust PAC Learning. CoRR (2020)
Steve Hanneke, Aryeh Kontorovich, Sivan Sabato, Roi Weiss
Universal Bayes Consistency in Metric Spaces. ITA (2020)
Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy
Proper Learning, Helly Number, and an Optimal SVM Bound. CoRR (2020)
Steve Hanneke, Samory Kpotufe
A No-Free-Lunch Theorem for MultiTask Learning. CoRR (2020)
Omar Montasser, Steve Hanneke, Nathan Srebro
VC Classes are Adversarially Robustly Learnable, but Only Improperly. CoRR (2019)
Steve Hanneke, Samory Kpotufe
On the Value of Target Data in Transfer Learning. NeurIPS (2019)
Steve Hanneke, Aryeh Kontorovich
Optimality of SVM: Novel proofs and tighter bounds. Theor. Comput. Sci. 796 (2019)
Steve Hanneke, Liu Yang
Statistical Learning under Nonstationary Mixing Processes. AISTATS (2019)
Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
Sample Compression for Real-Valued Learners. ALT (2019)
Steve Hanneke, Aryeh Kontorovich, Sivan Sabato, Roi Weiss
Universal Bayes consistency in metric spaces. CoRR (2019)
Omar Montasser, Steve Hanneke, Nathan Srebro
VC Classes are Adversarially Robustly Learnable, but Only Improperly. COLT (2019)
Steve Hanneke, Aryeh Kontorovich
A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes. ALT (2019)
Liu Yang, Steve Hanneke, Jaime G. Carbonell
Bounds on the minimax rate for estimating a prior over a VC class from independent learning tasks. Theor. Comput. Sci. 716 (2018)
Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
Agnostic Sample Compression for Linear Regression. CoRR (2018)
Steve Hanneke, Adam Kalai, Gautam Kamath, Christos Tzamos
Actively Avoiding Nonsense in Generative Models. CoRR (2018)
Nikita Zhivotovskiy, Steve Hanneke
Localization of VC classes: Beyond local Rademacher complexities. Theor. Comput. Sci. 742 (2018)
Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
Sample Compression for Real-Valued Learners. CoRR (2018)
Steve Hanneke, Aryeh Kontorovich
A New Lower Bound for Agnostic Learning with Sample Compression Schemes. CoRR (2018)
Steve Hanneke, Liu Yang
Testing piecewise functions. Theor. Comput. Sci. 745 (2018)
Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, Christos Tzamos
Actively Avoiding Nonsense in Generative Models. COLT (2018)
Steve Hanneke
Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. CoRR (2017)
Amit Dhurandhar, Steve Hanneke, Liu Yang
Learning with Changing Features. CoRR (2017)
Steve Hanneke, Liu Yang
Testing Piecewise Functions. CoRR (2017)
Steve Hanneke
The Optimal Sample Complexity of PAC Learning. J. Mach. Learn. Res. 17 (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Steve Hanneke