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Large-Scale Methods for Distributionally Robust Optimization
v1v2 (latest)

Large-Scale Methods for Distributionally Robust Optimization

12 October 2020
Daniel Levy
Y. Carmon
John C. Duchi
Aaron Sidford
ArXiv (abs)PDFHTML

Papers citing "Large-Scale Methods for Distributionally Robust Optimization"

40 / 140 papers shown
Calibrated Selective Classification
Calibrated Selective Classification
Adam Fisch
Tommi Jaakkola
Regina Barzilay
232
32
0
25 Aug 2022
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD
  Generalization
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization
Anshul Nasery
Sravanti Addepalli
Praneeth Netrapalli
Prateek Jain
OODFedML
171
1
0
19 Aug 2022
Rank-based Decomposable Losses in Machine Learning: A Survey
Rank-based Decomposable Losses in Machine Learning: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022
Shu Hu
Xin Wang
Siwei Lyu
328
36
0
18 Jul 2022
Breaking Correlation Shift via Conditional Invariant Regularizer
Breaking Correlation Shift via Conditional Invariant RegularizerInternational Conference on Learning Representations (ICLR), 2022
Mingyang Yi
Ruoyu Wang
Jiacheng Sun
Zhenguo Li
Zhi-Ming Ma
OODD
199
5
0
14 Jul 2022
Learning Debiased Classifier with Biased Committee
Learning Debiased Classifier with Biased CommitteeNeural Information Processing Systems (NeurIPS), 2022
Nayeong Kim
Sehyun Hwang
SungSoo Ahn
Jaesik Park
Suha Kwak
CML
451
64
0
22 Jun 2022
Learning Fair Representation via Distributional Contrastive
  Disentanglement
Learning Fair Representation via Distributional Contrastive DisentanglementKnowledge Discovery and Data Mining (KDD), 2022
Changdae Oh
Heeji Won
Junhyuk So
Taero Kim
Yewon Kim
Hosik Choi
Kyungwoo Song
FaMLCoGeCMLDRL
192
51
0
17 Jun 2022
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization
RECAPP: Crafting a More Efficient Catalyst for Convex OptimizationInternational Conference on Machine Learning (ICML), 2022
Y. Carmon
A. Jambulapati
Yujia Jin
Aaron Sidford
313
16
0
17 Jun 2022
Fairness and Explainability in Automatic Decision-Making Systems. A
  challenge for computer science and law
Fairness and Explainability in Automatic Decision-Making Systems. A challenge for computer science and lawEURO Journal on Decision Processes (EJDP), 2022
Thierry Kirat
Olivia Tambou
Virginie Do
A. Tsoukiás
FaML
126
22
0
14 May 2022
Learning to Split for Automatic Bias Detection
Learning to Split for Automatic Bias Detection
Yujia Bao
Regina Barzilay
219
21
0
28 Apr 2022
Improved Group Robustness via Classifier Retraining on Independent
  Splits
Improved Group Robustness via Classifier Retraining on Independent Splits
Thien Hai Nguyen
Hongyang R. Zhang
Huy Le Nguyen
OOD
341
2
0
20 Apr 2022
Distributionally Robust Models with Parametric Likelihood Ratios
Distributionally Robust Models with Parametric Likelihood RatiosInternational Conference on Learning Representations (ICLR), 2022
Paul Michel
Tatsunori Hashimoto
Graham Neubig
OOD
200
20
0
13 Apr 2022
Unbiased Multilevel Monte Carlo methods for intractable distributions:
  MLMC meets MCMC
Unbiased Multilevel Monte Carlo methods for intractable distributions: MLMC meets MCMCJournal of machine learning research (JMLR), 2022
Guanyang Wang
T. Wang
347
17
0
11 Apr 2022
Distributionally Robust Optimization via Ball Oracle Acceleration
Distributionally Robust Optimization via Ball Oracle AccelerationNeural Information Processing Systems (NeurIPS), 2022
Y. Carmon
Danielle Hausler
161
14
0
24 Mar 2022
Wasserstein Distributionally Robust Optimization with Wasserstein
  Barycenters
Wasserstein Distributionally Robust Optimization with Wasserstein Barycenters
Tim Tsz-Kit Lau
Han Liu
OOD
253
3
0
23 Mar 2022
Challenges and Strategies in Cross-Cultural NLP
Challenges and Strategies in Cross-Cultural NLPAnnual Meeting of the Association for Computational Linguistics (ACL), 2022
Daniel Hershcovich
Stella Frank
Heather Lent
Miryam de Lhoneux
Mostafa Abdou
...
Ruixiang Cui
Constanza Fierro
Katerina Margatina
Phillip Rust
Anders Søgaard
333
232
0
18 Mar 2022
Learning Distributionally Robust Models at Scale via Composite
  Optimization
Learning Distributionally Robust Models at Scale via Composite OptimizationInternational Conference on Learning Representations (ICLR), 2022
Farzin Haddadpour
Mohammad Mahdi Kamani
M. Mahdavi
Amin Karbasi
OOD
165
5
0
17 Mar 2022
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with
  Non-Convex Convergence Guarantee
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence GuaranteeInternational Conference on Machine Learning (ICML), 2022
Dixian Zhu
Gang Li
Bokun Wang
Xiaodong Wu
Tianbao Yang
550
37
0
01 Mar 2022
Adapting to Mixing Time in Stochastic Optimization with Markovian Data
Adapting to Mixing Time in Stochastic Optimization with Markovian DataInternational Conference on Machine Learning (ICML), 2022
Ron Dorfman
Kfir Y. Levy
380
36
0
09 Feb 2022
Diversify and Disambiguate: Learning From Underspecified Data
Diversify and Disambiguate: Learning From Underspecified Data
Yoonho Lee
Huaxiu Yao
Chelsea Finn
464
73
0
07 Feb 2022
Towards Group Robustness in the presence of Partial Group Labels
Towards Group Robustness in the presence of Partial Group Labels
Vishnu Suresh Lokhande
Kihyuk Sohn
Chang Jo Kim
Madeleine Udell
Chen-Yu Lee
Tomas Pfister
OOD
186
11
0
10 Jan 2022
BARACK: Partially Supervised Group Robustness With Guarantees
BARACK: Partially Supervised Group Robustness With Guarantees
N. Sohoni
Maziar Sanjabi
Nicolas Ballas
Aditya Grover
Shaoliang Nie
Hamed Firooz
Christopher Ré
OOD
282
28
0
31 Dec 2021
Label Distributionally Robust Losses for Multi-class Classification:
  Consistency, Robustness and Adaptivity
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and AdaptivityInternational Conference on Machine Learning (ICML), 2021
Dixian Zhu
Yiming Ying
Tianbao Yang
310
15
0
30 Dec 2021
Constrained Instance and Class Reweighting for Robust Learning under
  Label Noise
Constrained Instance and Class Reweighting for Robust Learning under Label Noise
Abhishek Kumar
Ehsan Amid
NoLa
168
20
0
09 Nov 2021
Coordinate Linear Variance Reduction for Generalized Linear Programming
Coordinate Linear Variance Reduction for Generalized Linear ProgrammingNeural Information Processing Systems (NeurIPS), 2021
Chaobing Song
Cheuk Yin Lin
Stephen J. Wright
Jelena Diakonikolas
317
14
0
02 Nov 2021
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis
Non-convex Distributionally Robust Optimization: Non-asymptotic AnalysisNeural Information Processing Systems (NeurIPS), 2021
Jikai Jin
Samir Bhatt
Haiyang Wang
Liwei Wang
321
55
0
24 Oct 2021
Balancing Average and Worst-case Accuracy in Multitask Learning
Balancing Average and Worst-case Accuracy in Multitask Learning
Paul Michel
Sebastian Ruder
Dani Yogatama
194
13
0
12 Oct 2021
Sinkhorn Distributionally Robust Optimization
Sinkhorn Distributionally Robust OptimizationOperational Research (OR), 2021
Jie Wang
Rui Gao
Yao Xie
528
47
0
24 Sep 2021
Distributionally Robust Multilingual Machine Translation
Distributionally Robust Multilingual Machine TranslationConference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Chunting Zhou
Daniel Levy
Xian Li
Marjan Ghazvininejad
Graham Neubig
207
24
0
09 Sep 2021
Towards Out-Of-Distribution Generalization: A Survey
Towards Out-Of-Distribution Generalization: A Survey
Tianyu Wang
Zheyan Shen
Yue He
Xingxuan Zhang
Renzhe Xu
Han Yu
Peng Cui
CMLOOD
504
628
0
31 Aug 2021
Just Train Twice: Improving Group Robustness without Training Group
  Information
Just Train Twice: Improving Group Robustness without Training Group InformationInternational Conference on Machine Learning (ICML), 2021
Emmy Liu
Behzad Haghgoo
Annie S. Chen
Aditi Raghunathan
Pang Wei Koh
Shiori Sagawa
Abigail Z. Jacobs
Chelsea Finn
OOD
323
642
0
19 Jul 2021
Stochastic Bias-Reduced Gradient Methods
Stochastic Bias-Reduced Gradient MethodsNeural Information Processing Systems (NeurIPS), 2021
Hilal Asi
Y. Carmon
A. Jambulapati
Yujia Jin
Aaron Sidford
195
33
0
17 Jun 2021
Frank-Wolfe Methods in Probability Space
Frank-Wolfe Methods in Probability Space
Carson Kent
Jose H. Blanchet
Peter Glynn
145
9
0
11 May 2021
Adversarially Robust Kernel Smoothing
Adversarially Robust Kernel SmoothingInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Jia-Jie Zhu
Christina Kouridi
Yassine Nemmour
Bernhard Schölkopf
269
8
0
16 Feb 2021
Achieving Efficiency in Black Box Simulation of Distribution Tails with
  Self-structuring Importance Samplers
Achieving Efficiency in Black Box Simulation of Distribution Tails with Self-structuring Importance SamplersOperational Research (OR), 2021
Anand Deo
Karthyek Murthy
244
13
0
14 Feb 2021
Risk-Averse Offline Reinforcement Learning
Risk-Averse Offline Reinforcement LearningInternational Conference on Learning Representations (ICLR), 2021
Núria Armengol Urpí
Sebastian Curi
Andreas Krause
OffRL
190
76
0
10 Feb 2021
Attentional-Biased Stochastic Gradient Descent
Attentional-Biased Stochastic Gradient Descent
Q. Qi
Yi Tian Xu
Rong Jin
W. Yin
Tianbao Yang
ODL
452
13
0
13 Dec 2020
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained
  Classification Problems
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification ProblemsNeural Information Processing Systems (NeurIPS), 2020
N. Sohoni
Jared A. Dunnmon
Geoffrey Angus
Albert Gu
Christopher Ré
274
278
0
25 Nov 2020
Coping with Label Shift via Distributionally Robust Optimisation
Coping with Label Shift via Distributionally Robust OptimisationInternational Conference on Learning Representations (ICLR), 2020
J.N. Zhang
A. Menon
Andreas Veit
Srinadh Bhojanapalli
Sanjiv Kumar
S. Sra
OOD
308
77
0
23 Oct 2020
An Online Method for A Class of Distributionally Robust Optimization
  with Non-Convex Objectives
An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectives
Qi Qi
Zhishuai Guo
Yi Tian Xu
Rong Jin
Tianbao Yang
391
51
0
17 Jun 2020
Device Heterogeneity in Federated Learning: A Superquantile Approach
Device Heterogeneity in Federated Learning: A Superquantile ApproachMachine-mediated learning (ML), 2020
Yassine Laguel
Krishna Pillutla
J. Malick
Zaïd Harchaoui
FedML
206
30
0
25 Feb 2020
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