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2020 – today
- 2024
[c64]Pramith Devulapalli, Steve Hanneke:
The Dimension of Self-Directed Learning. ALT 2024: 544-573- [c63]Steve Hanneke, Aryeh Kontorovich, Guy Kornowski:
Efficient Agnostic Learning with Average Smoothness. ALT 2024: 719-731 - [c62]Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas:
Universal Rates for Regression: Separations between Cut-Off and Absolute Loss. COLT 2024: 359-405 - [c61]Zachary Chase, Bogdan Chornomaz, Steve Hanneke, Shay Moran, Amir Yehudayoff:
Dual VC Dimension Obstructs Sample Compression by Embeddings. COLT 2024: 923-946 - [c60]Steve Hanneke:
The Star Number and Eluder Dimension: Elementary Observations About the Dimensions of Disagreement. COLT 2024: 2308-2359 - [c59]Steve Hanneke, Shay Moran, Tom Waknine:
List Sample Compression and Uniform Convergence. COLT 2024: 2360-2388 - [c58]Steve Hanneke, Shay Moran, Tom Waknine:
Open problem: Direct Sums in Learning Theory. COLT 2024: 5325-5329 - [c57]Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi:
Agnostic Sample Compression Schemes for Regression. ICML 2024 - [i57]Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran:
Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs. CoRR abs/2402.07453 (2024) - [i56]Pramith Devulapalli, Steve Hanneke:
The Dimension of Self-Directed Learning. CoRR abs/2402.13400 (2024) - [i55]Steve Hanneke, Shay Moran, Tom Waknine:
List Sample Compression and Uniform Convergence. CoRR abs/2403.10889 (2024) - [i54]Zachary Chase, Bogdan Chornomaz, Steve Hanneke, Shay Moran, Amir Yehudayoff:
Dual VC Dimension Obstructs Sample Compression by Embeddings. CoRR abs/2405.17120 (2024) - [i53]Simone Fioravanti, Steve Hanneke, Shay Moran, Hilla Schefler, Iska Tsubari:
Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem. CoRR abs/2407.07765 (2024) - [i52]Steve Hanneke, Kasper Green Larsen, Nikita Zhivotovskiy:
Revisiting Agnostic PAC Learning. CoRR abs/2407.19777 (2024) - [i51]Steve Hanneke, Samory Kpotufe:
A More Unified Theory of Transfer Learning. CoRR abs/2408.16189 (2024) - 2023
- [c56]Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran:
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. COLT 2023: 773-836 - [c55]Nataly Brukhim, Steve Hanneke, Shay Moran:
Improper Multiclass Boosting. COLT 2023: 5433-5452 - [c54]Steve Hanneke, Shay Moran, Qian Zhang:
Universal Rates for Multiclass Learning. COLT 2023: 5615-5681 - [c53]Steve Hanneke, Shay Moran, Vinod Raman, Unique Subedi, Ambuj Tewari:
Multiclass Online Learning and Uniform Convergence. COLT 2023: 5682-5696 - [c52]Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh:
Limits of Model Selection under Transfer Learning. COLT 2023: 5781-5812 - [c51]Steve Hanneke, Liu Yang:
Bandit Learnability can be Undecidable. COLT 2023: 5813-5849 - [c50]Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin:
Fine-Grained Distribution-Dependent Learning Curves. COLT 2023: 5890-5924 - [c49]Idan Attias, Steve Hanneke:
Adversarially Robust PAC Learnability of Real-Valued Functions. ICML 2023: 1172-1199 - [c48]Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas:
Optimal Learners for Realizable Regression: PAC Learning and Online Learning. NeurIPS 2023 - [c47]Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma:
Reliable learning in challenging environments. NeurIPS 2023 - [c46]Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty:
Adversarial Resilience in Sequential Prediction via Abstention. NeurIPS 2023 - [c45]Steve Hanneke, Shay Moran, Jonathan Shafer:
A Trichotomy for Transductive Online Learning. NeurIPS 2023 - [c44]Guy Kornowski, Steve Hanneke, Aryeh Kontorovich:
Near-optimal learning with average Hölder smoothness. NeurIPS 2023 - [i50]Moïse Blanchard, Steve Hanneke, Patrick Jaillet:
Contextual Bandits and Optimistically Universal Learning. CoRR abs/2301.00241 (2023) - [i49]Steve Hanneke, Aryeh Kontorovich, Guy Kornowski:
Near-optimal learning with average Hölder smoothness. CoRR abs/2302.06005 (2023) - [i48]Moïse Blanchard, Steve Hanneke, Patrick Jaillet:
Non-stationary Contextual Bandits and Universal Learning. CoRR abs/2302.07186 (2023) - [i47]Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran:
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. CoRR abs/2302.13849 (2023) - [i46]Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma:
Reliable Learning for Test-time Attacks and Distribution Shift. CoRR abs/2304.03370 (2023) - [i45]Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh:
Limits of Model Selection under Transfer Learning. CoRR abs/2305.00152 (2023) - [i44]Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty:
Adversarial Resilience in Sequential Prediction via Abstention. CoRR abs/2306.13119 (2023) - [i43]Steve Hanneke, Shay Moran, Qian Zhang:
Universal Rates for Multiclass Learning. CoRR abs/2307.02066 (2023) - [i42]Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas:
Optimal Learners for Realizable Regression: PAC Learning and Online Learning. CoRR abs/2307.03848 (2023) - [i41]Steve Hanneke, Aryeh Kontorovich, Guy Kornowski:
Efficient Agnostic Learning with Average Smoothness. CoRR abs/2309.17016 (2023) - [i40]Steve Hanneke, Shay Moran, Jonathan Shafer:
A Trichotomy for Transductive Online Learning. CoRR abs/2311.06428 (2023) - 2022
- [c43]Omar Montasser, Steve Hanneke, Nathan Srebro:
Transductive Robust Learning Guarantees. AISTATS 2022: 11461-11471 - [c42]Moïse Blanchard, Romain Cosson, Steve Hanneke:
Universal Online Learning with Unbounded Losses: Memory Is All You Need. ALT 2022: 107-127 - [c41]Steve Hanneke:
Universally Consistent Online Learning with Arbitrarily Dependent Responses. ALT 2022: 488-497 - [c40]Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma:
Robustly-reliable learners under poisoning attacks. COLT 2022: 4498-4534 - [c39]Idan Attias, Steve Hanneke, Yishay Mansour:
A Characterization of Semi-Supervised Adversarially Robust PAC Learnability. NeurIPS 2022 - [c38]Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran:
On Optimal Learning Under Targeted Data Poisoning. NeurIPS 2022 - [c37]Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas:
Universal Rates for Interactive Learning. NeurIPS 2022 - [c36]Omar Montasser, Steve Hanneke, Nati Srebro:
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. NeurIPS 2022 - [i39]Moïse Blanchard, Romain Cosson, Steve Hanneke:
Universal Online Learning with Unbounded Losses: Memory Is All You Need. CoRR abs/2201.08903 (2022) - [i38]Idan Attias, Steve Hanneke, Yishay Mansour:
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability. CoRR abs/2202.05420 (2022) - [i37]Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma:
Robustly-reliable learners under poisoning attacks. CoRR abs/2203.04160 (2022) - [i36]Steve Hanneke:
Universally Consistent Online Learning with Arbitrarily Dependent Responses. CoRR abs/2203.06046 (2022) - [i35]Idan Attias, Steve Hanneke:
Adversarially Robust Learning of Real-Valued Functions. CoRR abs/2206.12977 (2022) - [i34]Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin:
Fine-Grained Distribution-Dependent Learning Curves. CoRR abs/2208.14615 (2022) - [i33]Omar Montasser, Steve Hanneke, Nathan Srebro:
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. CoRR abs/2209.07369 (2022) - [i32]Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran:
On Optimal Learning Under Targeted Data Poisoning. CoRR abs/2210.02713 (2022) - 2021
- [j14]Steve Hanneke:
Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. J. Mach. Learn. Res. 22: 130:1-130:116 (2021) - [c35]Steve Hanneke, Liu Yang:
Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries. AISTATS 2021: 3997-4005 - [c34]Steve Hanneke, Aryeh Kontorovich:
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound. ALT 2021: 697-721 - [c33]Avrim Blum, Steve Hanneke, Jian Qian, Han Shao:
Robust learning under clean-label attack. COLT 2021: 591-634 - [c32]Steve Hanneke, Roi Livni, Shay Moran:
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. COLT 2021: 2289-2314 - [c31]Omar Montasser, Steve Hanneke, Nathan Srebro:
Adversarially Robust Learning with Unknown Perturbation Sets. COLT 2021: 3452-3482 - [c30]Steve Hanneke:
Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible? COLT 2021: 4642-4646 - [c29]Noga Alon, Steve Hanneke, Ron Holzman, Shay Moran:
A Theory of PAC Learnability of Partial Concept Classes. FOCS 2021: 658-671 - [c28]Olivier Bousquet, Steve Hanneke, Shay Moran
, Ramon van Handel, Amir Yehudayoff:
A theory of universal learning. STOC 2021: 532-541 - [i31]Steve Hanneke, Roi Livni, Shay Moran:
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games. CoRR abs/2102.01646 (2021) - [i30]Omar Montasser, Steve Hanneke, Nathan Srebro:
Adversarially Robust Learning with Unknown Perturbation Sets. CoRR abs/2102.02145 (2021) - [i29]Avrim Blum, Steve Hanneke, Jian Qian, Han Shao:
Robust learning under clean-label attack. CoRR abs/2103.00671 (2021) - [i28]Noga Alon, Steve Hanneke, Ron Holzman, Shay Moran:
A Theory of PAC Learnability of Partial Concept Classes. CoRR abs/2107.08444 (2021) - [i27]Steve Hanneke:
Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible? CoRR abs/2107.09542 (2021) - [i26]Omar Montasser, Steve Hanneke, Nathan Srebro:
Transductive Robust Learning Guarantees. CoRR abs/2110.10602 (2021) - 2020
- [j13]Steve Hanneke, Lev Reyzin:
Special issue on ALT 2017: Guest Editors' Introduction. Theor. Comput. Sci. 808: 1 (2020) - [c27]Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy:
Proper Learning, Helly Number, and an Optimal SVM Bound. COLT 2020: 582-609 - [c26]Steve Hanneke:
Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. ITA 2020: 1-95 - [c25]Steve Hanneke, Aryeh Kontorovich, Sivan Sabato, Roi Weiss:
Universal Bayes Consistency in Metric Spaces. ITA 2020: 1-33 - [c24]Omar Montasser, Steve Hanneke, Nati Srebro:
Reducing Adversarially Robust Learning to Non-Robust PAC Learning. NeurIPS 2020 - [i25]Steve Hanneke, Samory Kpotufe:
On the Value of Target Data in Transfer Learning. CoRR abs/2002.04747 (2020) - [i24]Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy:
Proper Learning, Helly Number, and an Optimal SVM Bound. CoRR abs/2005.11818 (2020) - [i23]Steve Hanneke, Samory Kpotufe:
A No-Free-Lunch Theorem for MultiTask Learning. CoRR abs/2006.15785 (2020) - [i22]Omar Montasser, Steve Hanneke, Nathan Srebro:
Reducing Adversarially Robust Learning to Non-Robust PAC Learning. CoRR abs/2010.12039 (2020) - [i21]Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff:
A Theory of Universal Learning. CoRR abs/2011.04483 (2020) - [i20]Steve Hanneke, Aryeh Kontorovich:
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound. CoRR abs/2011.04586 (2020)
2010 – 2019
- 2019
- [j12]Steve Hanneke, Aryeh Kontorovich:
Optimality of SVM: Novel proofs and tighter bounds. Theor. Comput. Sci. 796: 99-113 (2019) - [c23]Steve Hanneke, Liu Yang:
Statistical Learning under Nonstationary Mixing Processes. AISTATS 2019: 1678-1686 - [c22]Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi:
Sample Compression for Real-Valued Learners. ALT 2019: 466-488 - [c21]Steve Hanneke, Aryeh Kontorovich:
A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes. ALT 2019: 489-505 - [c20]Omar Montasser, Steve Hanneke, Nathan Srebro:
VC Classes are Adversarially Robustly Learnable, but Only Improperly. COLT 2019: 2512-2530 - [c19]Steve Hanneke, Samory Kpotufe:
On the Value of Target Data in Transfer Learning. NeurIPS 2019: 9867-9877 - [i19]Omar Montasser, Steve Hanneke, Nathan Srebro:
VC Classes are Adversarially Robustly Learnable, but Only Improperly. CoRR abs/1902.04217 (2019) - [i18]Steve Hanneke, Aryeh Kontorovich, Sivan Sabato, Roi Weiss:
Universal Bayes consistency in metric spaces. CoRR abs/1906.09855 (2019) - 2018
- [j11]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: 124-140 (2018) - [j10]Nikita Zhivotovskiy, Steve Hanneke:
Localization of VC classes: Beyond local Rademacher complexities. Theor. Comput. Sci. 742: 27-49 (2018) - [j9]Steve Hanneke, Liu Yang:
Testing piecewise functions. Theor. Comput. Sci. 745: 23-35 (2018) - [c18]Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, Christos Tzamos:
Actively Avoiding Nonsense in Generative Models. COLT 2018: 209-227 - [i17]Steve Hanneke, Adam Kalai, Gautam Kamath, Christos Tzamos:
Actively Avoiding Nonsense in Generative Models. CoRR abs/1802.07229 (2018) - [i16]Steve Hanneke, Aryeh Kontorovich:
A New Lower Bound for Agnostic Learning with Sample Compression Schemes. CoRR abs/1805.08140 (2018) - [i15]Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi:
Sample Compression for Real-Valued Learners. CoRR abs/1805.08254 (2018) - [i14]Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi:
Agnostic Sample Compression for Linear Regression. CoRR abs/1810.01864 (2018) - 2017
- [e1]Steve Hanneke, Lev Reyzin:
International Conference on Algorithmic Learning Theory, ALT 2017, 15-17 October 2017, Kyoto University, Kyoto, Japan. Proceedings of Machine Learning Research 76, PMLR 2017 [contents] - [i13]Amit Dhurandhar, Steve Hanneke, Liu Yang:
Learning with Changing Features. CoRR abs/1705.00219 (2017) - [i12]Steve Hanneke:
Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. CoRR abs/1706.01418 (2017) - [i11]Steve Hanneke, Liu Yang:
Testing Piecewise Functions. CoRR abs/1706.07669 (2017) - 2016
- [j8]Steve Hanneke:
The Optimal Sample Complexity of PAC Learning. J. Mach. Learn. Res. 17: 38:1-38:15 (2016) - [j7]Steve Hanneke:
Refined Error Bounds for Several Learning Algorithms. J. Mach. Learn. Res. 17: 135:1-135:55 (2016) - [c17]Nikita Zhivotovskiy, Steve Hanneke:
Localization of VC Classes: Beyond Local Rademacher Complexities. ALT 2016: 18-33 - 2015
- [j6]Yair Wiener, Steve Hanneke, Ran El-Yaniv:
A compression technique for analyzing disagreement-based active learning. J. Mach. Learn. Res. 16: 713-745 (2015) - [j5]Steve Hanneke, Liu Yang:
Minimax analysis of active learning. J. Mach. Learn. Res. 16: 3487-3602 (2015) - [c16]Steve Hanneke, Varun Kanade, Liu Yang:
Learning with a Drifting Target Concept. ALT 2015: 149-164 - [c15]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. ALT 2015: 270-284 - [i10]Steve Hanneke, Varun Kanade, Liu Yang:
Learning with a Drifting Target Concept. CoRR abs/1505.05215 (2015) - [i9]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. CoRR abs/1505.05231 (2015) - [i8]Steve Hanneke:
The Optimal Sample Complexity of PAC Learning. CoRR abs/1507.00473 (2015) - [i7]Steve Hanneke:
Refined Error Bounds for Several Learning Algorithms. CoRR abs/1512.07146 (2015) - [i6]Steve Hanneke, Tommi S. Jaakkola, Liu Yang:
Statistical Learning under Nonstationary Mixing Processes. CoRR abs/1512.08064 (2015) - 2014
- [j4]Steve Hanneke:
Theory of Disagreement-Based Active Learning. Found. Trends Mach. Learn. 7(2-3): 131-309 (2014) - [i5]Yair Wiener, Steve Hanneke, Ran El-Yaniv:
A Compression Technique for Analyzing Disagreement-Based Active Learning. CoRR abs/1404.1504 (2014) - [i4]Steve Hanneke, Liu Yang:
Minimax Analysis of Active Learning. CoRR abs/1410.0996 (2014) - 2013
- [j3]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
A theory of transfer learning with applications to active learning. Mach. Learn. 90(2): 161-189 (2013) - [c14]Liu Yang, Steve Hanneke:
Activized Learning with Uniform Classification Noise. ICML (2) 2013: 370-378 - 2012
- [j2]Steve Hanneke:
Activized Learning: Transforming Passive to Active with Improved Label Complexity. J. Mach. Learn. Res. 13: 1469-1587 (2012) - [c13]Maria-Florina Balcan, Steve Hanneke:
Robust Interactive Learning. COLT 2012: 20.1-20.34 - [i3]Steve Hanneke, Liu Yang:
Surrogate Losses in Passive and Active Learning. CoRR abs/1207.3772 (2012) - 2011
- [c12]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning. COLT 2011: 789-806 - [c11]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
The Sample Complexity of Self-Verifying Bayesian Active Learning. AISTATS 2011: 816-822 - [i2]Steve Hanneke:
Activized Learning: Transforming Passive to Active with Improved Label Complexity. CoRR abs/1108.1766 (2011) - [i1]Maria-Florina Balcan, Steve Hanneke:
Robust Interactive Learning. CoRR abs/1111.1422 (2011) - 2010
- [j1]Maria-Florina Balcan, Steve Hanneke, Jennifer Wortman Vaughan:
The true sample complexity of active learning. Mach. Learn. 80(2-3): 111-139 (2010) - [c10]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Bayesian Active Learning Using Arbitrary Binary Valued Queries. ALT 2010: 50-58 - [c9]Steve Hanneke, Liu Yang:
Negative Results for Active Learning with Convex Losses. AISTATS 2010: 321-325
2000 – 2009
- 2009
- [c8]Steve Hanneke:
Adaptive Rates of Convergence in Active Learning. COLT 2009 - [c7]Steve Hanneke, Eric P. Xing:
Network Completion and Survey Sampling. AISTATS 2009: 209-215 - 2008
- [c6]Maria-Florina Balcan, Steve Hanneke, Jennifer Wortman:
The True Sample Complexity of Active Learning. COLT 2008: 45-56 - 2007
- [c5]Steve Hanneke:
Teaching Dimension and the Complexity of Active Learning. COLT 2007: 66-81 - [c4]Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing:
Recovering temporally rewiring networks: a model-based approach. ICML 2007: 321-328 - [c3]Steve Hanneke:
A bound on the label complexity of agnostic active learning. ICML 2007: 353-360 - 2006
- [c2]Steve Hanneke, Eric P. Xing:
Discrete Temporal Models of Social Networks. SNA@ICML 2006: 115-125 - [c1]Steve Hanneke:
An analysis of graph cut size for transductive learning. ICML 2006: 393-399
Coauthor Index
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