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Universal Online Learning with Unbounded Losses: Memory Is All You Need

Universal Online Learning with Unbounded Losses: Memory Is All You Need

International Conference on Algorithmic Learning Theory (ALT), 2022
21 January 2022
Moise Blanchard
Romain Cosson
Steve Hanneke
ArXiv (abs)PDFHTMLHuggingFace (1 upvotes)

Papers citing "Universal Online Learning with Unbounded Losses: Memory Is All You Need"

7 / 7 papers shown
A Theory of Optimistically Universal Online Learnability for General Concept Classes
A Theory of Optimistically Universal Online Learnability for General Concept ClassesNeural Information Processing Systems (NeurIPS), 2025
Steve Hanneke
Hongao Wang
282
0
0
15 Jan 2025
Contextual Bandits and Optimistically Universal Learning
Contextual Bandits and Optimistically Universal Learning
Moise Blanchard
Steve Hanneke
Patrick Jaillet
OffRL
228
3
0
31 Dec 2022
Multiclass Learnability Beyond the PAC Framework: Universal Rates and
  Partial Concept Classes
Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept ClassesNeural Information Processing Systems (NeurIPS), 2022
Alkis Kalavasis
Grigoris Velegkas
Amin Karbasi
312
15
0
05 Oct 2022
Universal Regression with Adversarial Responses
Universal Regression with Adversarial ResponsesAnnals of Statistics (Ann. Stat.), 2022
Moise Blanchard
Patrick Jaillet
383
7
0
09 Mar 2022
Metric-valued regression
Metric-valued regression
Daniel Cohen
A. Kontorovich
FedML
285
6
0
07 Feb 2022
Universal Online Learning: an Optimistically Universal Learning Rule
Universal Online Learning: an Optimistically Universal Learning RuleAnnual Conference Computational Learning Theory (COLT), 2022
Moise Blanchard
370
14
0
16 Jan 2022
Universal Online Learning with Bounded Loss: Reduction to Binary
  Classification
Universal Online Learning with Bounded Loss: Reduction to Binary ClassificationAnnual Conference Computational Learning Theory (COLT), 2021
Moise Blanchard
Romain Cosson
268
10
0
29 Dec 2021
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