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Don't fear the unlabelled: safe semi-supervised learning via simple
  debiasing
v1v2v3 (latest)

Don't fear the unlabelled: safe semi-supervised learning via simple debiasing

International Conference on Learning Representations (ICLR), 2022
14 March 2022
Hugo Schmutz
O. Humbert
Pierre-Alexandre Mattei
ArXiv (abs)PDFHTMLGithub (7★)

Papers citing "Don't fear the unlabelled: safe semi-supervised learning via simple debiasing"

5 / 5 papers shown
Do We Really Even Need Data? A Modern Look at Drawing Inference with Predicted Data
Do We Really Even Need Data? A Modern Look at Drawing Inference with Predicted Data
Stephen Salerno
Kentaro Hoffman
Awan Afiaz
Anna Neufeld
Tyler H. McCormick
J. Leek
140
1
0
05 Dec 2025
Theory-inspired Label Shift Adaptation via Aligned Distribution Mixture
Theory-inspired Label Shift Adaptation via Aligned Distribution Mixture
Ruidong Fan
Xiao Ouyang
Hong Tao
Yuhua Qian
Chenping Hou
OOD
350
1
0
04 Nov 2024
Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning
Towards Understanding Why FixMatch Generalizes Better Than Supervised LearningInternational Conference on Learning Representations (ICLR), 2024
Jingyang Li
Jiachun Pan
Vincent Y. F. Tan
Kim-Chuan Toh
Pan Zhou
AAMLMLT
576
4
0
15 Oct 2024
Enhancing Semi-Supervised Learning via Representative and Diverse Sample
  Selection
Enhancing Semi-Supervised Learning via Representative and Diverse Sample SelectionNeural Information Processing Systems (NeurIPS), 2024
Qian Shao
Jiangrui Kang
Qiyuan Chen
Zepeng Li
Hongxia Xu
Yiwen Cao
Jiajuan Liang
Jian Wu
287
2
0
18 Sep 2024
Prediction De-Correlated Inference: A safe approach for post-prediction
  inference
Prediction De-Correlated Inference: A safe approach for post-prediction inference
Feng Gan
Wanfeng Liang
Changliang Zou
312
13
0
11 Dec 2023
1
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