Fetching the paper…
Reading the bibliography…
We study distributionally robust optimization with Sinkhorn distance -- a variant of Wasserstein distance based on entropic regularization.
Yule GU (1912) On the methods of measuring association between two attributes. Journal of the Royal Statistical Society
1912
Earlier work this paper cites.
Kruithof J (1937) Telefoonverkeersrekening. De Ingenieur
1937
Earlier work this paper cites.
Deming WE, Stephan FF (1940) On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. The Annals of Mathematical Statistics
1940
Earlier work this paper cites.
Scarf H (1957) A min-max solution of an inventory problem. Studies in the mathematical theory of inventory and production
1957
Earlier work this paper cites.
Fréchet M (1960) Sur les tableaux dont les marges et des bornes sont données. Revue de l’Institut international de statistique
1960
Earlier work this paper cites.
Blackwell D, Ryll-Nardzewski C (1963) Non-existence of everywhere proper conditional distributions. The Annals of Mathematical Statistics
1963
Earlier work this paper cites.
Sinkhorn R (1964) A relationship between arbitrary positive matrices and doubly stochastic matrices. The annals of mathematical statistics
1964
Earlier work this paper cites.
Bacharach M (1965) Estimating nonnegative matrices from marginal data. International Economic Review
1965
Earlier work this paper cites.
Altschuler J, Weed J, Rigollet P (2017) Near-linear time approximation algorithms for optimal transport via sinkhorn iteration. Advances in Neural Information Processing Systems
1971
Earlier work this paper cites.
Nemirovsky A, Yudin D (1983) Problem complexity and method efficiency in optimization. John Wiley & Sons
1983
Earlier work this paper cites.
Härdle W (1990) Applied nonparametric regression
1990
Earlier work this paper cites.
Nesterov Y, Nemirovskii A (1994) Interior-point polynomial algorithms in convex programming
1994
Earlier work this paper cites.
Vandenberghe L, Boyd S (1995) Semidefinite programming. SIAM review
1995
Earlier work this paper cites.
Kallenberg O (1997) Foundations of modern probability
1997
Earlier work this paper cites.
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceedings of the IEEE
1998
Earlier work this paper cites.
Rockafellar RT, Uryasev S, et al. (1999) Optimization of conditional value-at-risk. Journal of risk
1999
Earlier work this paper cites.
Shapiro A (2001) On duality theory of conic linear problems. Semi-infinite programming
2001
Earlier work this paper cites.
Kleywegt AJ, Shapiro A, Homem-de Mello T (2002) The sample average approximation method for stochastic discrete optimization. SIAM Journal on optimization
2002
Earlier work this paper cites.
Popescu I (2005) A semidefinite programming approach to optimal-moment bounds for convex classes of distributions. Mathematics of Operations Research
2005
Earlier work this paper cites.
Bertsimas D, Natarajan K, Teo CP (2006) Persistence in discrete optimization under data uncertainty. Mathematical programming
2006
Earlier work this paper cites.
Cover TM, Thomas JA (2006) Elements of Information Theory
2006
Earlier work this paper cites.
Asmussen S, Glynn PW (2007) Stochastic simulation: algorithms and analysis
2007
Earlier work this paper cites.
Pflug G, Wozabal D (2007) Ambiguity in portfolio selection. Quantitative Finance
2007
Earlier work this paper cites.
Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. Technical report, Citeseer
2009
Earlier work this paper cites.
Natarajan K, Song M, Teo CP (2009) Persistency model and its applications in choice modeling. Management Science
2009
Earlier work this paper cites.
Nemirovski A, Juditsky A, Lan G, Shapiro A (2009) Robust stochastic approximation approach to stochastic programming. SIAM Journal on optimization
2009
Earlier work this paper cites.
Delage E, Ye Y (2010) Distributionally robust optimization under moment uncertainty with application to data-driven problems. Operations Research
2010
Earlier work this paper cites.
Goh J, Sim M (2010) Distributionally robust optimization and its tractable approximations. Operations Research
2010
Earlier work this paper cites.
Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST)
2011
Earlier work this paper cites.
Coates A, Ng AY (2011) Analysis of large-scale visual recognition. Advances in neural information processing systems
2011
Earlier work this paper cites.
Agrawal S, Ding Y, Saberi A, Ye Y (2012) Price of correlations in stochastic optimization. Operations Research
2012
Earlier work this paper cites.
Doan XV, Natarajan K (2012) On the complexity of nonoverlapping multivariate marginal bounds for probabilistic combinatorial optimization problems. Operations research
2012
Earlier work this paper cites.
Hu Z, Hong LJ (2012) Kullback-leibler divergence constrained distributionally robust optimization. Optimization Online preprint Optimization Online:2012/11/3677
2012
Earlier work this paper cites.
Wozabal D (2012) A framework for optimization under ambiguity. Annals of Operations Research
2012
Earlier work this paper cites.
Ben-Tal A, den Hertog D, De Waegenaere A, Melenberg B, Rennen G (2013) Robust solutions of optimization problems affected by uncertain probabilities. Management Science
2013
Earlier work this paper cites.
Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems
2013
Earlier work this paper cites.
Zymler S, Kuhn D, Rustem B (2013) Distributionally robust joint chance constraints with second-order moment information. Mathematical Programming
2013
Earlier work this paper cites.
Courty N, Flamary R, Tuia D (2014) Domain adaptation with regularized optimal transport. Joint European Conference on Machine Learning and Knowledge Discovery in Databases
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
TinyImageNet (2014) TinyImageNet Visual Recognition Challenge
2014
Cited alongside, same era.
Wiesemann W, Kuhn D, Sim M (2014) Distributionally robust convex optimization. Operations Research
2014
Cited alongside, same era.
Bayraksan G, Love DK (2015) Data-driven stochastic programming using phi-divergences. The Operations Research Revolution
2015
Cited alongside, same era.
Blanchet JH, Glynn PW (2015) Unbiased monte carlo for optimization and functions of expectations via multi-level randomization. 2015 Winter Simulation Conference (WSC)
2015
Cited alongside, same era.
Shafieezadeh Abadeh S, Mohajerin Esfahani PM, Kuhn D (2015) Distributionally robust logistic regression. Advances in Neural Information Processing Systems
2015
Cited alongside, same era.
Staib M, Jegelka S (2019) Distributionally robust optimization and generalization in kernel methods. Advances in Neural Information Processing Systems
2019
Later among the works it cites.
Xie W (2019) On distributionally robust chance constrained programs with wasserstein distance. Mathematical Programming
2019
Later among the works it cites.
Bai Y, Wu X, Ozgur A (2020) Information constrained optimal transport: From talagrand, to marton, to cover. 2020 IEEE International Symposium on Information Theory (ISIT)
2020
Later among the works it cites.
Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Science
2020
Later among the works it cites.
Blanchet J, Kang Y (2020) Semi-supervised learning based on distributionally robust optimization. Data Analysis and Applications 3: Computational, Classification, Financial, Statistical and Stochastic Methods
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Van Parys BP, Goulart PJ, Kuhn D (2015) Generalized gauss inequalities via semidefinite programming. Mathematical Programming
2015
Cited alongside, same era.
Wang Z, Glynn PW, Ye Y (2015) Likelihood robust optimization for data-driven problems. Computational Management Science
2015
Cited alongside, same era.
Cohen MB, Lee YT, Miller G, Pachocki J, Sidford A (2016) Geometric median in nearly linear time. Proceedings of the forty-eighth annual ACM symposium on Theory of Computing
2016
Cited alongside, same era.
Courty N, Flamary R, Tuia D, Rakotomamonjy A (2016) Optimal transport for domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence
2016
Cited alongside, same era.
Genevay A, Cuturi M, Peyré G, Bach F (2016) Stochastic optimization for large-scale optimal transport. Advances in Neural Information Processing Systems
2016
Cited alongside, same era.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Cited alongside, same era.
Namkoong H, Duchi JC (2016) Stochastic gradient methods for distributionally robust optimization with f-divergences. Advances in Neural Information Processing Systems
2016
Cited alongside, same era.
2020
Later among the works it cites.
Chen Y, Sun H, Xu H (2020) Decomposition and discrete approximation methods for solving two-stage distributionally robust optimization problems. Computational Optimization and Applications
2020
Later among the works it cites.
Eckstein S, Kupper M, Pohl M (2020) Robust risk aggregation with neural networks. Mathematical Finance
2020
Later among the works it cites.
Levy D, Carmon Y, Duchi JC, Sidford A (2020) Large-scale methods for distributionally robust optimization. Advances in Neural Information Processing Systems
2020
Later among the works it cites.
Lin T, Fan C, Ho N, Cuturi M, Jordan M (2020) Projection robust wasserstein distance and riemannian optimization. Advances in Neural Information Processing Systems
2020
Later among the works it cites.
Mensch A, Peyré G (2020) Online sinkhorn: Optimal transport distances from sample streams. Advances in Neural Information Processing Systems
2020
Later among the works it cites.
Nguyen VA, Si N, Blanchet J (2020) Robust bayesian classification using an optimistic score ratio. International Conference on Machine Learning
2020
Later among the works it cites.
Patrini G, van den Berg R, Forre P, Carioni M, Bhargav S, Welling M, Genewein T, Nielsen F (2020) Sinkhorn autoencoders. Uncertainty in Artificial Intelligence
2020
Later among the works it cites.
Yang I (2020) Wasserstein distributionally robust stochastic control: A data-driven approach. IEEE Transactions on Automatic Control
2020
Later among the works it cites.
ApS M (2021) Mosek modeling cookbook 3.2.3. https://docs.mosek.com/modeling-cookbook/index.html#
2021
Closest in time.
Blanchet J, Murthy K, Nguyen VA (2021) Statistical analysis of wasserstein distributionally robust estimators. Tutorials in Operations Research: Emerging Optimization Methods and Modeling Techniques with Applications
2021
Closest in time.
Duchi JC, Glynn PW, Namkoong H (2021) Statistics of robust optimization: A generalized empirical likelihood approach. Mathematics of Operations Research
2021
Closest in time.
Hu Y, Chen X, He N (2021) On the bias-variance-cost tradeoff of stochastic optimization. Advances in Neural Information Processing Systems
2021
Closest in time.
Huang M, Ma S, Lai L (2021) A riemannian block coordinate descent method for computing the projection robust wasserstein distance. Proceedings of the 38th International Conference on Machine Learning
2021
Closest in time.
Liu Y, Yuan X, Zhang J (2021) Discrete approximation scheme in distributionally robust optimization. Numer Math Theory Methods Appl
2021
Closest in time.
Pichler A, Shapiro A (2021) Mathematical foundations of distributionally robust multistage optimization. SIAM Journal on Optimization
2021
Closest in time.
Singh D, Zhang S (2021) Distributionally robust profit opportunities. Operations Research Letters
2021
Closest in time.
Zhu J, Jitkrittum W, Diehl M, Schölkopf B (2021) Kernel distributionally robust optimization: Generalized duality theorem and stochastic approximation. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics
2021
Closest in time.
Chen Z, Kuhn D, Wiesemann W (2022) Data-driven chance constrained programs over wasserstein balls. Operations Research
2022
Closest in time.
Gao R (2022) Finite-sample guarantees for wasserstein distributionally robust optimization: Breaking the curse of dimensionality. Operations Research
2022
Closest in time.
Gao R, Chen X, Kleywegt AJ (2022) Wasserstein distributionally robust optimization and variation regularization. Operations Research
2022
Closest in time.
Gao R, Kleywegt A (2022) Distributionally robust stochastic optimization with wasserstein distance. Mathematics of Operations Research
2022
Closest in time.
Li J, Lin S, Blanchet J, Nguyen VA (2022) Tikhonov regularization is optimal transport robust under martingale constraints. Advances in Neural Information Processing Systems
2022
Closest in time.
Lin T, Ho N, Jordan MI (2022) On the efficiency of entropic regularized algorithms for optimal transport. Journal of Machine Learning Research
2022
Closest in time.
Selvi A, Belbasi MR, Haugh MB, Wiesemann W (2022) Wasserstein logistic regression with mixed features. Advances in Neural Information Processing Systems
2022
Closest in time.
Singh D, Zhang S (2022) Tight bounds for a class of data-driven distributionally robust risk measures. Applied Mathematics & Optimization
2022
Closest in time.
Yu Y, Lin T, Mazumdar EV, Jordan M (2022) Fast distributionally robust learning with variance-reduced min-max optimization. International Conference on Artificial Intelligence and Statistics
2022
Closest in time.
Azizian W, Iutzeler F, Malick J (2023) Regularization for wasserstein distributionally robust optimization. ESAIM: Control, Optimisation and Calculus of Variations
2023
Closest in time.
Qi Q, Lyu J, Chan KS, Bai EW, Yang T (2023) Stochastic constrained DRO with a complexity independent of sample size. Transactions on Machine Learning Research
2023
Closest in time.
2023
Closest in time.
Shapiro A, Zhou E, Lin Y (2023) Bayesian distributionally robust optimization. SIAM Journal on Optimization
2023
Closest in time.
Song J, He N, Ding L, Zhao C (2023) Provably convergent policy optimization via metric-aware trust region methods. Transactions on Machine Learning Research
2023
Closest in time.
2024
Closest in time.
Nguyen VA, Zhang F, Wang S, Blanchet J, Delage E, Ye Y (2024) Robustifying conditional portfolio decisions via optimal transport. Operations Research
2024
Closest in time.