Steve Hanneke

According to our database1, Steve Hanneke authored at least 91 papers between 2006 and 2024.

Collaborative distances:
  • Dijkstra number2 of three.
  • Erdős number3 of two.

Timeline

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Bibliography

2024
Multiclass Transductive Online Learning.
CoRR, 2024

Sample Compression Scheme Reductions.
CoRR, 2024

A Complete Characterization of Learnability for Stochastic Noisy Bandits.
CoRR, 2024

A More Unified Theory of Transfer Learning.
CoRR, 2024

Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs.
CoRR, 2024

Agnostic Sample Compression Schemes for Regression.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Revisiting Agnostic PAC Learning.
Proceedings of the 65th IEEE Annual Symposium on Foundations of Computer Science, 2024

Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem.
Proceedings of the 65th IEEE Annual Symposium on Foundations of Computer Science, 2024

Open problem: Direct Sums in Learning Theory.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

List Sample Compression and Uniform Convergence.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

The Star Number and Eluder Dimension: Elementary Observations About the Dimensions of Disagreement.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Universal Rates for Regression: Separations between Cut-Off and Absolute Loss.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Dual VC Dimension Obstructs Sample Compression by Embeddings.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Efficient Agnostic Learning with Average Smoothness.
Proceedings of the International Conference on Algorithmic Learning Theory, 2024

The Dimension of Self-Directed Learning.
Proceedings of the International Conference on Algorithmic Learning Theory, 2024

2023
Reliable Learning for Test-time Attacks and Distribution Shift.
CoRR, 2023

Non-stationary Contextual Bandits and Universal Learning.
CoRR, 2023

Contextual Bandits and Optimistically Universal Learning.
CoRR, 2023

Near-optimal learning with average Hölder smoothness.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Trichotomy for Transductive Online Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adversarial Resilience in Sequential Prediction via Abstention.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Reliable learning in challenging environments.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Optimal Learners for Realizable Regression: PAC Learning and Online Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adversarially Robust PAC Learnability of Real-Valued Functions.
Proceedings of the International Conference on Machine Learning, 2023

Universal Rates for Multiclass Learning.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Multiclass Online Learning and Uniform Convergence.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Limits of Model Selection under Transfer Learning.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Bandit Learnability can be Undecidable.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Improper Multiclass Boosting.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Fine-Grained Distribution-Dependent Learning Curves.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Adversarially Robust Learning of Real-Valued Functions.
CoRR, 2022

Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Universal Rates for Interactive Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Optimal Learning Under Targeted Data Poisoning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Characterization of Semi-Supervised Adversarially Robust PAC Learnability.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Robustly-reliable learners under poisoning attacks.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Universally Consistent Online Learning with Arbitrarily Dependent Responses.
Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

Universal Online Learning with Unbounded Losses: Memory Is All You Need.
Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

Transductive Robust Learning Guarantees.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes.
J. Mach. Learn. Res., 2021

A theory of universal learning.
Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

A Theory of PAC Learnability of Partial Concept Classes.
Proceedings of the 62nd IEEE Annual Symposium on Foundations of Computer Science, 2021

Adversarially Robust Learning with Unknown Perturbation Sets.
Proceedings of the Conference on Learning Theory, 2021

Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games.
Proceedings of the Conference on Learning Theory, 2021

Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible?
Proceedings of the Conference on Learning Theory, 2021

Robust learning under clean-label attack.
Proceedings of the Conference on Learning Theory, 2021

Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound.
Proceedings of the Algorithmic Learning Theory, 2021

Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Special issue on ALT 2017: Guest Editors' Introduction.
Theor. Comput. Sci., 2020

A No-Free-Lunch Theorem for MultiTask Learning.
CoRR, 2020

Reducing Adversarially Robust Learning to Non-Robust PAC Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Universal Bayes Consistency in Metric Spaces.
Proceedings of the Information Theory and Applications Workshop, 2020

Proper Learning, Helly Number, and an Optimal SVM Bound.
Proceedings of the Conference on Learning Theory, 2020

2019
Optimality of SVM: Novel proofs and tighter bounds.
Theor. Comput. Sci., 2019

On the Value of Target Data in Transfer Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

VC Classes are Adversarially Robustly Learnable, but Only Improperly.
Proceedings of the Conference on Learning Theory, 2019

Sample Compression for Real-Valued Learners.
Proceedings of the Algorithmic Learning Theory, 2019

A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes.
Proceedings of the Algorithmic Learning Theory, 2019

Statistical Learning under Nonstationary Mixing Processes.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Localization of VC classes: Beyond local Rademacher complexities.
Theor. Comput. Sci., 2018

Bounds on the minimax rate for estimating a prior over a VC class from independent learning tasks.
Theor. Comput. Sci., 2018

Testing piecewise functions.
Theor. Comput. Sci., 2018

Agnostic Sample Compression for Linear Regression.
CoRR, 2018

A New Lower Bound for Agnostic Learning with Sample Compression Schemes.
CoRR, 2018

Actively Avoiding Nonsense in Generative Models.
Proceedings of the Conference On Learning Theory, 2018

2017
Learning with Changing Features.
CoRR, 2017

2016
Refined Error Bounds for Several Learning Algorithms.
J. Mach. Learn. Res., 2016

The Optimal Sample Complexity of PAC Learning.
J. Mach. Learn. Res., 2016

2015
A compression technique for analyzing disagreement-based active learning.
J. Mach. Learn. Res., 2015

Minimax analysis of active learning.
J. Mach. Learn. Res., 2015

Statistical Learning under Nonstationary Mixing Processes.
CoRR, 2015

Learning with a Drifting Target Concept.
Proceedings of the Algorithmic Learning Theory - 26th International Conference, 2015

2014
Theory of Disagreement-Based Active Learning.
Found. Trends Mach. Learn., 2014

2013
A theory of transfer learning with applications to active learning.
Mach. Learn., 2013

Activized Learning with Uniform Classification Noise.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Activized Learning: Transforming Passive to Active with Improved Label Complexity.
J. Mach. Learn. Res., 2012

Robust Interactive Learning.
Proceedings of the COLT 2012, 2012

Surrogate Losses in Passive and Active Learning
CoRR, 2012

2011
Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning.
Proceedings of the COLT 2011, 2011

The Sample Complexity of Self-Verifying Bayesian Active Learning.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

2010
The true sample complexity of active learning.
Mach. Learn., 2010

Negative Results for Active Learning with Convex Losses.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Bayesian Active Learning Using Arbitrary Binary Valued Queries.
Proceedings of the Algorithmic Learning Theory, 21st International Conference, 2010

2009
Network Completion and Survey Sampling.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Adaptive Rates of Convergence in Active Learning.
Proceedings of the COLT 2009, 2009

2007
A bound on the label complexity of agnostic active learning.
Proceedings of the Machine Learning, 2007

Recovering temporally rewiring networks: a model-based approach.
Proceedings of the Machine Learning, 2007

Teaching Dimension and the Complexity of Active Learning.
Proceedings of the Learning Theory, 20th Annual Conference on Learning Theory, 2007

2006
Discrete Temporal Models of Social Networks.
Proceedings of the Statistical Network Analysis: Models, Issues, and New Directions, 2006

An analysis of graph cut size for transductive learning.
Proceedings of the Machine Learning, 2006


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