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Self-training Converts Weak Learners to Strong Learners in Mixture
  Models

Self-training Converts Weak Learners to Strong Learners in Mixture Models

25 June 2021
Spencer Frei
Difan Zou
Zixiang Chen
Quanquan Gu
ArXivPDFHTML

Papers citing "Self-training Converts Weak Learners to Strong Learners in Mixture Models"

12 / 12 papers shown
Title
High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws
High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws
M. E. Ildiz
Halil Alperen Gozeten
Ege Onur Taga
Marco Mondelli
Samet Oymak
54
2
0
24 Oct 2024
Theoretical Analysis of Weak-to-Strong Generalization
Theoretical Analysis of Weak-to-Strong Generalization
Hunter Lang
David Sontag
Aravindan Vijayaraghavan
25
19
0
25 May 2024
Can semi-supervised learning use all the data effectively? A lower bound
  perspective
Can semi-supervised learning use all the data effectively? A lower bound perspective
Alexandru cTifrea
Gizem Yüce
Amartya Sanyal
Fanny Yang
36
3
0
30 Nov 2023
Learning with Explanation Constraints
Learning with Explanation Constraints
Rattana Pukdee
Dylan Sam
J. Zico Kolter
Maria-Florina Balcan
Pradeep Ravikumar
FAtt
32
6
0
25 Mar 2023
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with
  Public Unlabeled Data
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data
Jinyan Su
Jinhui Xu
Di Wang
18
2
0
17 Sep 2022
Self-Training: A Survey
Self-Training: A Survey
Massih-Reza Amini
Vasilii Feofanov
Loïc Pauletto
Lies Hadjadj
Emilie Devijver
Yury Maximov
SSL
28
102
0
24 Feb 2022
Cycle Self-Training for Domain Adaptation
Cycle Self-Training for Domain Adaptation
Hong Liu
Jianmin Wang
Mingsheng Long
25
174
0
05 Mar 2021
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label
  Selection Framework for Semi-Supervised Learning
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
Mamshad Nayeem Rizve
Kevin Duarte
Y. S. Rawat
M. Shah
220
508
0
15 Jan 2021
Provable Generalization of SGD-trained Neural Networks of Any Width in
  the Presence of Adversarial Label Noise
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
Spencer Frei
Yuan Cao
Quanquan Gu
FedML
MLT
64
18
0
04 Jan 2021
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for
  Out-of-Distribution Robustness
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness
Sang Michael Xie
Ananya Kumar
Robbie Jones
Fereshte Khani
Tengyu Ma
Percy Liang
OOD
166
62
0
08 Dec 2020
Meta Pseudo Labels
Meta Pseudo Labels
Hieu H. Pham
Zihang Dai
Qizhe Xie
Minh-Thang Luong
Quoc V. Le
VLM
253
656
0
23 Mar 2020
Confidence Regularized Self-Training
Confidence Regularized Self-Training
Yang Zou
Zhiding Yu
Xiaofeng Liu
B. Kumar
Jinsong Wang
230
789
0
26 Aug 2019
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