Semi-Supervised Empirical Risk Minimization: When can unlabeled data improve prediction?

Oren Yuval, Saharon Rosset

Type: Preprint

Publication Date: 2020-09-01

Citations: 1

Locations

arXiv (Cornell University) - View

Similar Works

Action	Title	Year	Authors
+ PDF Chat	Semi-supervised empirical risk minimization: Using unlabeled data to improve prediction	2022	Oren Yuval Saharon Rosset
+ PDF Chat	Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning	2023	Siyi Deng Yang Ning Jiwei Zhao Heping Zhang
+	Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning	2020	Siyi Deng Ning Yang Jiwei Zhao Heping Zhang
+ PDF Chat	Semi-Supervised U-statistics	2024	Ilmun Kim Larry Wasserman Sivaraman Balakrishnan Matey Neykov
+ PDF Chat	Improved estimators for semi-supervised high-dimensional regression model	2022	Ilan Livne David Azriel Yair Goldberg
+	Optimal Semi-supervised Estimation and Inference for High-dimensional Linear Regression	2020	Siyi Deng Yang Ning Jiwei Zhao Heping Zhang
+	A General M-estimation Theory in Semi-Supervised Framework	2023	Shanshan Song Yuanyuan Lin Yong Zhou
+	A General M-estimation Theory in Semi-Supervised Framework	2023	Shanshan Song Yuanyuan Lin Yong Zhou
+ PDF Chat	Efficient and adaptive linear regression in semi-supervised settings	2018	Abhishek Chakrabortty Tianxi Cai
+ PDF Chat	Semi-supervised Regression Analysis with Model Misspecification and High-dimensional Data	2024	Ye Tian Peng Wu Zhiqiang Tan
+ PDF Chat	Projected estimators for robust semi-supervised classification	2017	Jesse H. Krijthe Marco Loog
+	Enhancing efficiency and robustness in high-dimensional linear regression with additional unlabeled data	2023	Kai Chen Yuqian Zhang
+	Can semi-supervised learning use all the data effectively? A lower bound perspective	2023	Alexandru Țifrea Gizem Yüce Amartya Sanyal Fanny Yang
+	Improved Estimators for Semi-supervised High-dimensional Regression Model	2021	Ilan Livne David Azriel Yair Goldberg
+	Mixed Semi-Supervised Generalized-Linear-Regression with applications to Deep-Learning and Interpolators	2023	Oren Yuval Saharon Rosset
+	The Effect of Model Misspecification on Semi-Supervised Classification	2011	Ting Yang Carey E. Priebe
+	How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?	2022	Haiyun He Gholamali Aminian Yuheng Bu Miguel Tréfaut Rodrigues Vincent Y. F. Tan
+	Risk bounds for PU learning under Selected At Random assumption	2022	Olivier Coudray Christine Keribin Pascal Massart Patrick Pamphile
+	Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach	2019	Nan Lu Tianyi Zhang Gang Niu Masashi Sugiyama
+	Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful	2020	Jingge Zhu

Works That Cite This (1)

Action	Title	Year	Authors
+ PDF Chat	Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review	2022	Daniel Ahfock Geoffrey J. McLachlan

Works Cited by This (14)

Action	Title	Year	Authors
+ PDF Chat	Behaviour of Third Order Terms in Quadratic Approximations of LR-Statistics in Multivariate Generalized Linear Models	1986	Christian Kredler
+	Assessing the quadratic approximation to the log likelihood function in nonnormal linear models	1983	Salomon Minkin
+ PDF Chat	An Ancillarity Paradox Which Appears in Multiple Linear Regression	1990	Lawrence D. Brown
+	Semi-Supervised linear regression	2016	David Azriel Lawrence D. Brown Michael Sklar Richard A. Berk Andreas Buja Linda Zhao
+ PDF Chat	Efficient and adaptive linear regression in semi-supervised settings	2018	Abhishek Chakrabortty Tianxi Cai
+ PDF Chat	From Fixed-X to Random-X Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation	2018	Saharon Rosset Ryan J. Tibshirani
+	De-Biasing The Lasso With Degrees-of-Freedom Adjustment	2019	Pierre Bellec Cun‐Hui Zhang
+ PDF Chat	Debiasing the lasso: Optimal sample size for Gaussian designs	2018	Adel Javanmard Andrea Montanari
+	On the prediction loss of the lasso in the partially labeled setting	2018	Pierre Bellec Arnak S. Dalalyan Edwin Grappin Quentin Paris
+ PDF Chat	Semi-supervised inference: General theory and estimation of means	2019	Anru R. Zhang Lawrence D. Brown Tianxi Cai