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. 2403.10175
  4. Cited By
A Short Survey on Importance Weighting for Machine Learning

A Short Survey on Importance Weighting for Machine Learning

15 March 2024
Masanari Kimura
H. Hino
ArXivPDFHTML

Papers citing "A Short Survey on Importance Weighting for Machine Learning"

50 / 58 papers shown
Title
Information Geometrically Generalized Covariate Shift Adaptation
Information Geometrically Generalized Covariate Shift Adaptation
Masanari Kimura
H. Hino
OOD
49
7
0
19 Apr 2023
Change is Hard: A Closer Look at Subpopulation Shift
Change is Hard: A Closer Look at Subpopulation Shift
Yuzhe Yang
Haoran Zhang
Dina Katabi
Marzyeh Ghassemi
OOD
54
105
0
23 Feb 2023
AdaFocal: Calibration-aware Adaptive Focal Loss
AdaFocal: Calibration-aware Adaptive Focal Loss
Arindam Ghosh
Thomas Schaaf
Matthew R. Gormley
FedML
UQCV
75
29
0
21 Nov 2022
Active Learning with Expected Error Reduction
Active Learning with Expected Error Reduction
Stephen Mussmann
Julia Reisler
Daniel Tsai
Ehsan Mousavi
S. O'Brien
M. Goldszmidt
UQCV
BDL
72
11
0
17 Nov 2022
Learning Sample Reweighting for Accuracy and Adversarial Robustness
Learning Sample Reweighting for Accuracy and Adversarial Robustness
Chester Holtz
Tsui-Wei Weng
Zhengchao Wan
OOD
69
4
0
20 Oct 2022
Alpha-divergence Variational Inference Meets Importance Weighted
  Auto-Encoders: Methodology and Asymptotics
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics
Kamélia Daudel
Joe Benton
Yuyang Shi
Arnaud Doucet
DRL
48
11
0
12 Oct 2022
Importance Tempering: Group Robustness for Overparameterized Models
Importance Tempering: Group Robustness for Overparameterized Models
Yiping Lu
Wenlong Ji
Zachary Izzo
Lexing Ying
64
7
0
19 Sep 2022
Domain Adaptation under Open Set Label Shift
Domain Adaptation under Open Set Label Shift
Saurabh Garg
Sivaraman Balakrishnan
Zachary Chase Lipton
OOD
VLM
54
42
0
26 Jul 2022
Adversarial Focal Loss: Asking Your Discriminator for Hard Examples
Adversarial Focal Loss: Asking Your Discriminator for Hard Examples
Chen Liu
Xiaomeng Dong
Michael Potter
Hsi-Ming Chang
Ravi Soni
49
2
0
15 Jul 2022
Generative Adversarial Networks
Generative Adversarial Networks
Gilad Cohen
Raja Giryes
GAN
280
30,103
0
01 Mar 2022
Cyclical Focal Loss
Cyclical Focal Loss
L. Smith
54
14
0
16 Feb 2022
Featurized Density Ratio Estimation
Featurized Density Ratio Estimation
Kristy Choi
Madeline Liao
Stefano Ermon
TPM
56
23
0
05 Jul 2021
Dynamic Weighted Learning for Unsupervised Domain Adaptation
Dynamic Weighted Learning for Unsupervised Domain Adaptation
Ning Xiao
Lei Zhang
42
145
0
22 Mar 2021
Fairness in Credit Scoring: Assessment, Implementation and Profit
  Implications
Fairness in Credit Scoring: Assessment, Implementation and Profit Implications
Nikita Kozodoi
Johannes Jacob
Stefan Lessmann
FaML
56
116
0
02 Mar 2021
On Statistical Bias In Active Learning: How and When To Fix It
On Statistical Bias In Active Learning: How and When To Fix It
Sebastian Farquhar
Y. Gal
Tom Rainforth
TDI
HAI
42
85
0
27 Jan 2021
Active Learning: Problem Settings and Recent Developments
Active Learning: Problem Settings and Recent Developments
H. Hino
34
41
0
08 Dec 2020
Suppressing Mislabeled Data via Grouping and Self-Attention
Suppressing Mislabeled Data via Grouping and Self-Attention
Xiaojiang Peng
Kai Wang
Zhaoyang Zeng
Qing Li
Jianfei Yang
Yu Qiao
40
32
0
29 Oct 2020
Large-Scale Methods for Distributionally Robust Optimization
Large-Scale Methods for Distributionally Robust Optimization
Daniel Levy
Y. Carmon
John C. Duchi
Aaron Sidford
76
217
0
12 Oct 2020
How Does Mixup Help With Robustness and Generalization?
How Does Mixup Help With Robustness and Generalization?
Linjun Zhang
Zhun Deng
Kenji Kawaguchi
Amirata Ghorbani
James Zou
AAML
84
251
0
09 Oct 2020
A Brief Review of Domain Adaptation
A Brief Review of Domain Adaptation
Abolfazl Farahani
Sahar Voghoei
Khaled Rasheed
H. Arabnia
OOD
50
542
0
07 Oct 2020
Fairness in Machine Learning: A Survey
Fairness in Machine Learning: A Survey
Simon Caton
C. Haas
FaML
102
640
0
04 Oct 2020
Contextual Diversity for Active Learning
Contextual Diversity for Active Learning
Sharat Agarwal
H. Arora
Saket Anand
Chetan Arora
127
169
0
13 Aug 2020
BREEDS: Benchmarks for Subpopulation Shift
BREEDS: Benchmarks for Subpopulation Shift
Shibani Santurkar
Dimitris Tsipras
Aleksander Madry
OOD
59
172
0
11 Aug 2020
Active Learning under Label Shift
Active Learning under Label Shift
Eric Zhao
Anqi Liu
Anima Anandkumar
Yisong Yue
78
26
0
16 Jul 2020
Learning from Noisy Labels with Deep Neural Networks: A Survey
Learning from Noisy Labels with Deep Neural Networks: A Survey
Hwanjun Song
Minseok Kim
Dongmin Park
Yooju Shin
Jae-Gil Lee
NoLa
109
987
0
16 Jul 2020
Fairness without Demographics through Adversarially Reweighted Learning
Fairness without Demographics through Adversarially Reweighted Learning
Preethi Lahoti
Alex Beutel
Jilin Chen
Kang Lee
Flavien Prost
Nithum Thain
Xuezhi Wang
Ed H. Chi
FaML
114
337
0
23 Jun 2020
Rethinking Importance Weighting for Deep Learning under Distribution
  Shift
Rethinking Importance Weighting for Deep Learning under Distribution Shift
Tongtong Fang
Nan Lu
Gang Niu
Masashi Sugiyama
62
139
0
08 Jun 2020
Generalized Focal Loss: Learning Qualified and Distributed Bounding
  Boxes for Dense Object Detection
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Xiang Li
Wenhai Wang
Lijun Wu
Shuo Chen
Xiaolin Hu
Jun Li
Jinhui Tang
Jian Yang
ObjD
71
1,199
0
08 Jun 2020
What Makes for Good Views for Contrastive Learning?
What Makes for Good Views for Contrastive Learning?
Yonglong Tian
Chen Sun
Ben Poole
Dilip Krishnan
Cordelia Schmid
Phillip Isola
SSL
100
1,329
0
20 May 2020
Supervised Contrastive Learning
Supervised Contrastive Learning
Prannay Khosla
Piotr Teterwak
Chen Wang
Aaron Sarna
Yonglong Tian
Phillip Isola
Aaron Maschinot
Ce Liu
Dilip Krishnan
SSL
149
4,547
0
23 Apr 2020
Towards Inheritable Models for Open-Set Domain Adaptation
Towards Inheritable Models for Open-Set Domain Adaptation
Jogendra Nath Kundu
Naveen Venkat
R. Ambareesh
V. RahulM.
R. Venkatesh Babu
VLM
55
119
0
09 Apr 2020
Calibrating Deep Neural Networks using Focal Loss
Calibrating Deep Neural Networks using Focal Loss
Jishnu Mukhoti
Viveka Kulharia
Amartya Sanyal
Stuart Golodetz
Philip Torr
P. Dokania
UQCV
81
463
0
21 Feb 2020
Domain Aggregation Networks for Multi-Source Domain Adaptation
Domain Aggregation Networks for Multi-Source Domain Adaptation
Junfeng Wen
Russell Greiner
Dale Schuurmans
55
71
0
11 Sep 2019
Are Anchor Points Really Indispensable in Label-Noise Learning?
Are Anchor Points Really Indispensable in Label-Noise Learning?
Xiaobo Xia
Tongliang Liu
N. Wang
Bo Han
Chen Gong
Gang Niu
Masashi Sugiyama
NoLa
73
379
0
01 Jun 2019
Active Adversarial Domain Adaptation
Active Adversarial Domain Adaptation
Jong-Chyi Su
Yi-Hsuan Tsai
Kihyuk Sohn
Buyu Liu
Subhransu Maji
Manmohan Chandraker
57
139
0
16 Apr 2019
Exploring Representativeness and Informativeness for Active Learning
Exploring Representativeness and Informativeness for Active Learning
Bo Du
Zengmao Wang
Lefei Zhang
Liangpei Zhang
Wen Liu
Jialie Shen
Dacheng Tao
37
173
0
14 Apr 2019
On The Power of Curriculum Learning in Training Deep Networks
On The Power of Curriculum Learning in Training Deep Networks
Guy Hacohen
D. Weinshall
ODL
72
445
0
07 Apr 2019
Learning to Transfer Examples for Partial Domain Adaptation
Learning to Transfer Examples for Partial Domain Adaptation
Zhangjie Cao
Kaichao You
Mingsheng Long
Jianmin Wang
Qiang Yang
62
274
0
28 Mar 2019
Evaluating model calibration in classification
Evaluating model calibration in classification
Juozas Vaicenavicius
David Widmann
Carl R. Andersson
Fredrik Lindsten
Jacob Roll
Thomas B. Schon
UQCV
157
198
0
19 Feb 2019
What is the Effect of Importance Weighting in Deep Learning?
What is the Effect of Importance Weighting in Deep Learning?
Jonathon Byrd
Zachary Chase Lipton
89
464
0
08 Dec 2018
Learning from positive and unlabeled data: a survey
Learning from positive and unlabeled data: a survey
Jessa Bekker
Jesse Davis
78
560
0
12 Nov 2018
Partial Adversarial Domain Adaptation
Partial Adversarial Domain Adaptation
Zhangjie Cao
Li-jie Ma
Mingsheng Long
Jianmin Wang
85
439
0
10 Aug 2018
Reacting to Variations in Product Demand: An Application for Conversion
  Rate (CR) Prediction in Sponsored Search
Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search
Marcelo Tallis
Pranjul Yadav
51
28
0
25 May 2018
Masking: A New Perspective of Noisy Supervision
Masking: A New Perspective of Noisy Supervision
Bo Han
Jiangchao Yao
Gang Niu
Mingyuan Zhou
Ivor Tsang
Ya Zhang
Masashi Sugiyama
NoLa
68
255
0
21 May 2018
Importance Weighted Adversarial Nets for Partial Domain Adaptation
Importance Weighted Adversarial Nets for Partial Domain Adaptation
Jing Zhang
Zewei Ding
W. Li
P. Ogunbona
74
420
0
25 Mar 2018
Learning to Reweight Examples for Robust Deep Learning
Learning to Reweight Examples for Robust Deep Learning
Mengye Ren
Wenyuan Zeng
Binh Yang
R. Urtasun
OOD
NoLa
143
1,426
0
24 Mar 2018
Risk and parameter convergence of logistic regression
Risk and parameter convergence of logistic regression
Ziwei Ji
Matus Telgarsky
73
130
0
20 Mar 2018
Detecting and Correcting for Label Shift with Black Box Predictors
Detecting and Correcting for Label Shift with Black Box Predictors
Zachary Chase Lipton
Yu Wang
Alex Smola
OOD
63
554
0
12 Feb 2018
Deep Visual Domain Adaptation: A Survey
Deep Visual Domain Adaptation: A Survey
Mei Wang
Weihong Deng
OOD
70
2,013
0
10 Feb 2018
mixup: Beyond Empirical Risk Minimization
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang
Moustapha Cissé
Yann N. Dauphin
David Lopez-Paz
NoLa
280
9,764
0
25 Oct 2017
12
Next