Steve Hanneke
77 papers · 2005–2026 · 6 venues · across top CS/AI venues
This profile combines several different researchers. Authors are keyed by name, and this name's papers fall into 22 separate collaboration groups - the largest holds only 48% of them. Pick the group below that matches the researcher you mean. How a shared name is split.
Achievements are not shown for a combined profile - each one describes a single researcher's record, which this is not.
Which Steve Hanneke?
7 groups of papers that share co-authors internally but none with each other - each most likely a different researcher. Pick one to see its papers.
with Shay Moran, Idan Attias, Aryeh Kontorovich
37 papers
· 2019–2025
· COLT, NeurIPS, ALT
Hide ▲
A Trichotomy for List Transductive Online Learning
ICML 2025
Data Selection for ERMs
COLT 2025
For Universal Multiclass Online Learning, Bandit Feedback and Full Supervision are Equivalent
ALT 2025
Open Problem: Data Selection for Regression Tasks
COLT 2025
Private List Learnability vs. Online List Learnability
COLT 2025
Representation Preserving Multiclass Agnostic to Realizable Reduction
ICML 2025
Sample Compression Scheme Reductions
ALT 2025
Universal Rates for Multiclass Learning with Bandit Feedback
COLT 2025
A Theory of Optimistically Universal Online Learnability for General Concept Classes
NeurIPS 2024
Agnostic Sample Compression Schemes for Regression
ICML 2024
Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs
NeurIPS 2024
Dual VC Dimension Obstructs Sample Compression by Embeddings
COLT 2024
Efficient Agnostic Learning with Average Smoothness
ALT 2024
Improved Sample Complexity for Multiclass PAC Learning
NeurIPS 2024
List Sample Compression and Uniform Convergence
COLT 2024
Multiclass Transductive Online Learning
NeurIPS 2024
Open problem: Direct Sums in Learning Theory
COLT 2024
Universal Rates for Active Learning
NeurIPS 2024
Universal Rates for Regression: Separations between Cut-Off and Absolute Loss
COLT 2024
A Trichotomy for Transductive Online Learning
NeurIPS 2023
Adversarial Resilience in Sequential Prediction via Abstention
NeurIPS 2023
Adversarially Robust PAC Learnability of Real-Valued Functions
ICML 2023
Fine-Grained Distribution-Dependent Learning Curves
COLT 2023
Improper Multiclass Boosting
COLT 2023
Multiclass Online Learning and Uniform Convergence
COLT 2023
Near-optimal learning with average Hölder smoothness
NeurIPS 2023
Optimal Learners for Realizable Regression: PAC Learning and Online Learning
NeurIPS 2023
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension
COLT 2023
Universal Rates for Multiclass Learning
COLT 2023
A Characterization of Semi-Supervised Adversarially Robust PAC Learnability
NeurIPS 2022
On Optimal Learning Under Targeted Data Poisoning
NeurIPS 2022
Universal Rates for Interactive Learning
NeurIPS 2022
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games
COLT 2021
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
ALT 2021
Proper Learning, Helly Number, and an Optimal SVM Bound
COLT 2020
A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes
ALT 2019
Sample Compression for Real-Valued Learners
ALT 2019
15 papers whose co-authors appear nowhere else under this name
Conference on Learning Theory 2026: Preface
COLT 2026
A Complete Characterization of Learnability for Stochastic Noisy Bandits
ALT 2025
The Star Number and Eluder Dimension: Elementary Observations About the Dimensions of Disagreement
COLT 2024
Universal Online Learning with Unbounded Losses: Memory Is All You Need
ALT 2022
Universally Consistent Online Learning with Arbitrarily Dependent Responses
ALT 2022
Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes
JMLR 2021
Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible?
COLT 2021
Actively Avoiding Nonsense in Generative Models
COLT 2018
Algorithmic Learning Theory (ALT) 2017: Preface
ALT 2017
Refined Error Bounds for Several Learning Algorithms
JMLR 2016
The Optimal Sample Complexity of PAC Learning
JMLR 2016
A Compression Technique for Analyzing Disagreement-Based Active Learning
JMLR 2015
Activized Learning: Transforming Passive to Active with Improved Label Complexity
JMLR 2012
Robust Interactive Learning
COLT 2012
Network Completion and Survey Sampling
AISTATS 2005