An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift

24 February 2022

Gholamali Aminian

Mahed Abroshan

Mohammad Mahdi Khalili

Papers citing "An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift"

20 / 20 papers shown

Title
Understanding the Capabilities and Limitations of Weak-to-Strong Generalization Wei Yao Wenkai Yang Z. Wang Yankai Lin Yong Liu ELM 90 1 0 03 Feb 2025
Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks Hai-Xiao Wang Zhichao Wang 71 1 0 18 Dec 2024
Theory-inspired Label Shift Adaptation via Aligned Distribution Mixture Ruidong Fan Xiao Ouyang Hong Tao Yuhua Qian Chenping Hou OOD 44 0 0 04 Nov 2024
Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation Jiachen Liang Ruibing Hou Hong Chang Bingpeng Ma Shiguang Shan Xilin Chen 19 4 0 31 May 2024
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift Mitsuhiro Fujikawa Yohei Akimoto Jun Sakuma Kazuto Fukuchi 24 0 0 27 May 2024
Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision Stefan Dietrich Julian Rodemann Christoph Jansen BDL 27 5 0 24 May 2024
Robust Semi-supervised Learning via $f$ -Divergence and $α$ -Rényi Divergence Gholamali Aminian Amirhossien Bagheri Mahyar JafariNodeh Radmehr Karimian Mohammad Hossein Yassaee 19 0 0 01 May 2024
Nearest Neighbor Sampling for Covariate Shift Adaptation Franccois Portier Lionel Truquet Ikko Yamane OffRL 18 0 0 15 Dec 2023
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications Huayi Tang Yong Liu 53 1 0 08 Nov 2023
Domain adaptation using optimal transport for invariant learning using histopathology datasets Kianoush Falahkheirkhah Alex X. Lu David Alvarez-Melis G. Huynh OOD MedIm 20 4 0 03 Mar 2023
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning Julian Rodemann Christoph Jansen G. Schollmeyer Thomas Augustin 18 0 0 02 Mar 2023
Approximately Bayes-Optimal Pseudo Label Selection Julian Rodemann Jann Goschenhofer Emilio Dorigatti T. Nagler Thomas Augustin 19 8 0 17 Feb 2023
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm? Haiyun He Gholamali Aminian Yuheng Bu Miguel R. D. Rodrigues Vincent Y. F. Tan 19 4 0 15 Oct 2022
Information-Theoretic Analysis of Unsupervised Domain Adaptation Ziqiao Wang Yongyi Mao 38 11 0 03 Oct 2022
Learning Algorithm Generalization Error Bounds via Auxiliary Distributions Gholamali Aminian Saeed Masiha Laura Toni M. Rodrigues 4 7 0 02 Oct 2022
Don't fear the unlabelled: safe semi-supervised learning via simple debiasing Hugo Schmutz O. Humbert Pierre-Alexandre Mattei 11 8 0 14 Mar 2022
Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning Haiyun He Hanshu Yan Vincent Y. F. Tan 11 11 0 03 Oct 2021
Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms Gholamali Aminian Laura Toni M. Rodrigues 62 16 0 03 Feb 2021
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 206 506 0 15 Jan 2021
Domain Adaptation: Learning Bounds and Algorithms Yishay Mansour M. Mohri Afshin Rostamizadeh 179 786 0 19 Feb 2009