ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1810.01545
  4. Cited By
A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation
v1v2 (latest)

A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation

3 October 2018
Clayton Scott
ArXiv (abs)PDFHTML

Papers citing "A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation"

27 / 27 papers shown
Title
Recalibrating binary probabilistic classifiers
Recalibrating binary probabilistic classifiers
Dirk Tasche
11
0
0
25 May 2025
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift
Baozhen Wang
Xingye Qiao
152
0
0
25 Feb 2025
Label Noise: Ignorance Is Bliss
Label Noise: Ignorance Is Bliss
Yilun Zhu
Jianxin Zhang
Aditya Gangrade
Clayton Scott
NoLa
94
2
0
31 Oct 2024
DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation
DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation
Qinshuo Liu
Zixin Wang
Xi-An Li
Xinyao Ji
Lei Zhang
Lin Liu
Zhonghua Liu
76
0
0
04 Aug 2024
Minimax And Adaptive Transfer Learning for Nonparametric Classification
  under Distributed Differential Privacy Constraints
Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints
Arnab Auddy
T. T. Cai
Abhinav Chakraborty
95
1
0
28 Jun 2024
An adaptive transfer learning perspective on classification in
  non-stationary environments
An adaptive transfer learning perspective on classification in non-stationary environments
Henry W J Reeve
86
0
0
28 May 2024
Distribution-Free Rates in Neyman-Pearson Classification
Distribution-Free Rates in Neyman-Pearson Classification
Mohammadreza M. Kalan
Samory Kpotufe
46
1
0
14 Feb 2024
Invariance assumptions for class distribution estimation
Invariance assumptions for class distribution estimation
Dirk Tasche
OOD
68
4
0
28 Nov 2023
Tight Rates in Supervised Outlier Transfer Learning
Tight Rates in Supervised Outlier Transfer Learning
Mohammadreza M. Kalan
Samory Kpotufe
33
1
0
07 Oct 2023
Efficient and Multiply Robust Risk Estimation under General Forms of
  Dataset Shift
Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift
Hongxiang Qiu
E. T. Tchetgen
Yan Sun
OOD
54
7
0
28 Jun 2023
Mixture Proportion Estimation Beyond Irreducibility
Mixture Proportion Estimation Beyond Irreducibility
Yilun Zhu
A. Fjeldsted
Darren C. Holland
George V. Landon
A. Lintereur
Clayton Scott
29
5
0
02 Jun 2023
Classification Tree Pruning Under Covariate Shift
Classification Tree Pruning Under Covariate Shift
Nicholas Galbraith
Samory Kpotufe
71
1
0
07 May 2023
Sparse joint shift in multinomial classification
Sparse joint shift in multinomial classification
Dirk Tasche
67
3
0
29 Mar 2023
Factorizable Joint Shift in Multinomial Classification
Factorizable Joint Shift in Multinomial Classification
Dirk Tasche
123
3
0
29 Jul 2022
Class Prior Estimation under Covariate Shift: No Problem?
Class Prior Estimation under Covariate Shift: No Problem?
Dirk Tasche
141
6
0
06 Jun 2022
Non-splitting Neyman-Pearson Classifiers
Non-splitting Neyman-Pearson Classifiers
Jingming Wang
Lucy Xia
Z. Bao
Xin Tong
56
1
0
01 Dec 2021
Adaptive transfer learning
Adaptive transfer learning
Henry W. J. Reeve
T. Cannings
R. Samworth
OOD
27
12
0
08 Jun 2021
Calibrating sufficiently
Calibrating sufficiently
Dirk Tasche
UQCV
48
11
0
15 May 2021
A Computationally Efficient Classification Algorithm in Posterior Drift
  Model: Phase Transition and Minimax Adaptivity
A Computationally Efficient Classification Algorithm in Posterior Drift Model: Phase Transition and Minimax Adaptivity
Ruiqi Liu
Kexuan Li
Zuofeng Shang
28
4
0
09 Nov 2020
On Localized Discrepancy for Domain Adaptation
On Localized Discrepancy for Domain Adaptation
Yuchen Zhang
Mingsheng Long
Jianmin Wang
Michael I. Jordan
73
18
0
14 Aug 2020
Self-Updating Models with Error Remediation
Self-Updating Models with Error Remediation
J. Doak
Michael R. Smith
J. Ingram
KELM
34
1
0
19 May 2020
On the Value of Target Data in Transfer Learning
On the Value of Target Data in Transfer Learning
Steve Hanneke
Samory Kpotufe
81
74
0
12 Feb 2020
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Antonin Berthon
Bo Han
Gang Niu
Tongliang Liu
Masashi Sugiyama
NoLa
132
108
0
11 Jan 2020
Learning from Multiple Corrupted Sources, with Application to Learning
  from Label Proportions
Learning from Multiple Corrupted Sources, with Application to Learning from Label Proportions
Clayton Scott
Jianxin Zhang
27
7
0
10 Oct 2019
Transfer Learning for Nonparametric Classification: Minimax Rate and
  Adaptive Classifier
Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifier
AI T.TONYC
EI Hongjiw
69
98
0
07 Jun 2019
Marginal Singularity, and the Benefits of Labels in Covariate-Shift
Marginal Singularity, and the Benefits of Labels in Covariate-Shift
Samory Kpotufe
Guillaume Martinet
171
96
0
05 Mar 2018
Domain Generalization by Marginal Transfer Learning
Domain Generalization by Marginal Transfer Learning
Gilles Blanchard
A. Deshmukh
Ürün Dogan
Gyemin Lee
Clayton Scott
OOD
102
286
0
21 Nov 2017
1