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1905.11866
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When can unlabeled data improve the learning rate?
Annual Conference Computational Learning Theory (COLT), 2019
28 May 2019
Christina Göpfert
Shai Ben-David
Olivier Bousquet
Sylvain Gelly
Ilya O. Tolstikhin
Ruth Urner
Re-assign community
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Papers citing
"When can unlabeled data improve the learning rate?"
14 / 14 papers shown
A Distributional-Lifting Theorem for PAC Learning
Annual Conference Computational Learning Theory (COLT), 2025
Guy Blanc
Jane Lange
Carmen Strassle
Li-Yang Tan
OODD
162
2
0
19 Jun 2025
Proper Learnability and the Role of Unlabeled Data
International Conference on Algorithmic Learning Theory (ALT), 2025
Julian Asilis
Siddartha Devic
S. Dughmi
Willie Neiswanger
S. Teng
282
0
0
14 Feb 2025
Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference
Yujin Han
Difan Zou
AAML
271
11
0
22 Apr 2024
Can semi-supervised learning use all the data effectively? A lower bound perspective
Neural Information Processing Systems (NeurIPS), 2023
Alexandru cTifrea
Gizem Yüce
Amartya Sanyal
Fanny Yang
281
6
0
30 Nov 2023
Lifting uniform learners via distributional decomposition
Symposium on the Theory of Computing (STOC), 2023
Guy Blanc
Jane Lange
Ali Malik
Li-Yang Tan
FedML
163
6
0
27 Mar 2023
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Haiyun He
Gholamali Aminian
Yuheng Bu
Miguel R. D. Rodrigues
Vincent Y. F. Tan
218
7
0
15 Oct 2022
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Gholamali Aminian
Mahed Abroshan
Mohammad Mahdi Khalili
Laura Toni
M. Rodrigues
OOD
289
29
0
24 Feb 2022
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability
Neural Information Processing Systems (NeurIPS), 2022
Idan Attias
Steve Hanneke
Yishay Mansour
291
17
0
11 Feb 2022
Federated Multi-Task Learning under a Mixture of Distributions
Neural Information Processing Systems (NeurIPS), 2021
Othmane Marfoq
Giovanni Neglia
A. Bellet
Laetitia Kameni
Richard Vidal
FedML
405
348
0
23 Aug 2021
Self-training Converts Weak Learners to Strong Learners in Mixture Models
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Spencer Frei
Difan Zou
Zixiang Chen
Quanquan Gu
313
22
0
25 Jun 2021
Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review
Econometrics and Statistics (ES), 2021
Daniel Ahfock
Geoffrey J. McLachlan
243
18
0
08 Apr 2021
Black-box Certification and Learning under Adversarial Perturbations
H. Ashtiani
Vinayak Pathak
Ruth Urner
AAML
188
20
0
30 Jun 2020
Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful
Jingge Zhu
SSL
79
10
0
22 May 2020
Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results
A. Mey
Marco Loog
SSL
248
20
0
26 Aug 2019
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