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Semi-supervised Learning based on Distributionally Robust Optimization
28 February 2017
Jose H. Blanchet
Yang Kang
OOD
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Papers citing
"Semi-supervised Learning based on Distributionally Robust Optimization"
21 / 21 papers shown
A stochastic smoothing framework for nonconvex-nonconcave min-sum-max problems with applications to Wasserstein distributionally robust optimization
Wei Liu
Muhammad Khan
Gabriel Mancino-Ball
Yangyang Xu
291
3
0
24 Feb 2025
Toward Robust Neural Reconstruction from Sparse Point Sets
Computer Vision and Pattern Recognition (CVPR), 2024
Amine Ouasfi
Shubhendu Jena
Eric Marchand
A. Boukhayma
326
5
0
20 Dec 2024
An Inexact Halpern Iteration with Application to Distributionally Robust Optimization
Ling Liang
Zusen Xu
Kim-Chuan Toh
Jia Jie Zhu
463
4
0
08 Feb 2024
Flow-based Distributionally Robust Optimization
IEEE Journal on Selected Areas in Information Theory (JSAIT), 2023
Chen Xu
Jonghyeok Lee
Xiuyuan Cheng
Yao Xie
OOD
579
12
0
30 Oct 2023
Out-Of-Domain Unlabeled Data Improves Generalization
International Conference on Learning Representations (ICLR), 2023
Amir Saberi
Amir Najafi
Alireza Heidari
Mohammad Hosein Movasaghinia
Abolfazl Motahari
B. Khalaj
OOD
454
3
0
29 Sep 2023
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits
Yi Shen
Pan Xu
Michael M. Zavlanos
OffRL
461
9
0
15 Sep 2023
Optimal Transport Model Distributional Robustness
Neural Information Processing Systems (NeurIPS), 2023
Van-Anh Nguyen
Trung Le
Anh Tuan Bui
Thanh-Toan Do
Dinh Q. Phung
OOD
350
7
0
07 Jun 2023
Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models
Neural Information Processing Systems (NeurIPS), 2023
Waïss Azizian
F. Iutzeler
J. Malick
OOD
416
13
0
26 May 2023
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training
International Conference on Learning Representations (ICLR), 2022
Tu Bui
Trung Le
Quan Hung Tran
He Zhao
Dinh Q. Phung
AAML
OOD
249
54
0
27 Feb 2022
Sinkhorn Distributionally Robust Optimization
Operational Research (OR), 2021
Jie Wang
Rui Gao
Yao Xie
636
52
0
24 Sep 2021
Stochastic Bias-Reduced Gradient Methods
Neural Information Processing Systems (NeurIPS), 2021
Hilal Asi
Y. Carmon
A. Jambulapati
Yujia Jin
Aaron Sidford
307
37
0
17 Jun 2021
Unbiased Gradient Estimation for Distributionally Robust Learning
Soumyadip Ghosh
M. Squillante
OOD
296
7
0
22 Dec 2020
Large-Scale Methods for Distributionally Robust Optimization
Daniel Levy
Y. Carmon
John C. Duchi
Aaron Sidford
381
255
0
12 Oct 2020
Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality
Operational Research (OR), 2020
Rui Gao
438
123
0
09 Sep 2020
Incorporating Unlabeled Data into Distributionally Robust Learning
Journal of machine learning research (JMLR), 2019
Charlie Frogner
Sebastian Claici
Edward Chien
Justin Solomon
OOD
249
28
0
16 Dec 2019
Distributionally Robust Optimization: A Review
Open Journal of Mathematical Optimization (OJMO), 2019
Hamed Rahimian
Sanjay Mehrotra
367
234
0
13 Aug 2019
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Neural Information Processing Systems (NeurIPS), 2019
Amir Najafi
S. Maeda
Masanori Koyama
Takeru Miyato
OOD
311
136
0
24 May 2019
Adversarial Risk Bounds via Function Transformation
Justin Khim
Po-Ling Loh
AAML
198
50
0
22 Oct 2018
Data-driven Optimal Cost Selection for Distributionally Robust Optimization
Jose H. Blanchet
Yang Kang
Fan Zhang
Karthyek Murthy
OOD
350
47
0
19 May 2017
Robust Wasserstein Profile Inference and Applications to Machine Learning
Jose H. Blanchet
Yang Kang
Karthyek Murthy
OOD
602
373
0
18 Oct 2016
Sample Out-Of-Sample Inference Based on Wasserstein Distance
Jose H. Blanchet
Yang Kang
348
39
0
04 May 2016
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