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Quantifying Distributional Model Risk via Optimal Transport

Quantifying Distributional Model Risk via Optimal Transport

5 April 2016
Jose H. Blanchet
Karthyek Murthy
ArXivPDFHTML

Papers citing "Quantifying Distributional Model Risk via Optimal Transport"

15 / 15 papers shown
Title
Wasserstein Distributionally Robust Regret Optimization
Wasserstein Distributionally Robust Regret Optimization
Lukas-Benedikt Fiechtner
Jose Blanchet
55
0
0
15 Apr 2025
A primer on optimal transport for causal inference with observational data
Florian F Gunsilius
OT
CML
108
0
0
10 Mar 2025
Universal generalization guarantees for Wasserstein distributionally robust models
Universal generalization guarantees for Wasserstein distributionally robust models
Tam Le
Jérome Malick
OOD
110
3
0
28 Jan 2025
Distributionally Robust Optimization
Distributionally Robust Optimization
Daniel Kuhn
Soroosh Shafiee
W. Wiesemann
77
0
0
04 Nov 2024
Robust Reinforcement Learning with Dynamic Distortion Risk Measures
Robust Reinforcement Learning with Dynamic Distortion Risk Measures
Anthony Coache
S. Jaimungal
76
1
0
16 Sep 2024
Rejection via Learning Density Ratios
Rejection via Learning Density Ratios
Alexander Soen
Hisham Husain
Philip Schulz
Vu-Linh Nguyen
114
2
0
29 May 2024
An Inexact Halpern Iteration with Application to Distributionally Robust Optimization
An Inexact Halpern Iteration with Application to Distributionally Robust Optimization
Ling Liang
Zusen Xu
Kim-Chuan Toh
Jia Jie Zhu
103
4
0
08 Feb 2024
Nash Equilibria, Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
Nash Equilibria, Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
Soroosh Shafieezadeh-Abadeh
Liviu Aolaritei
Florian Dorfler
Daniel Kuhn
119
21
0
07 Mar 2023
Sinkhorn Distributionally Robust Optimization
Sinkhorn Distributionally Robust Optimization
Jie Wang
Rui Gao
Yao Xie
95
38
0
24 Sep 2021
Robust Wasserstein Profile Inference and Applications to Machine
  Learning
Robust Wasserstein Profile Inference and Applications to Machine Learning
Jose H. Blanchet
Yang Kang
Karthyek Murthy
OOD
68
331
0
18 Oct 2016
Learning with a Wasserstein Loss
Learning with a Wasserstein Loss
Charlie Frogner
Chiyuan Zhang
H. Mobahi
Mauricio Araya-Polo
T. Poggio
57
602
0
17 Jun 2015
Data-driven Distributionally Robust Optimization Using the Wasserstein
  Metric: Performance Guarantees and Tractable Reformulations
Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations
Peyman Mohajerin Esfahani
Daniel Kuhn
77
1,660
0
19 May 2015
On the rate of convergence in Wasserstein distance of the empirical
  measure
On the rate of convergence in Wasserstein distance of the empirical measure
N. Fournier
Arnaud Guillin
179
1,142
0
07 Dec 2013
Learning Probability Measures with respect to Optimal Transport Metrics
Learning Probability Measures with respect to Optimal Transport Metrics
Guillermo D. Cañas
Lorenzo Rosasco
OT
85
100
0
05 Sep 2012
Convergence of latent mixing measures in finite and infinite mixture
  models
Convergence of latent mixing measures in finite and infinite mixture models
X. Nguyen
96
184
0
15 Sep 2011
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