Fetching the paper…
Reading the bibliography…
Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain.
R. Baire (1905), Leçons sur les Fonctions Discontinues
1905
Earlier work this paper cites.
J. L. W. V. Jensen (1906), Sur les fonctions convexes et les inégalités entre les valeurs moyennes, Acta Mathematica
1906
Earlier work this paper cites.
H. Hamburger (1920), Über eine Erweiterung des Stieltjesschen Momentenproblems, Mathematische Annalen
1920
Earlier work this paper cites.
H. Jeffreys and D. Wrinch (1921), On certain fundamental principles of scientific enquiry, Philosophical Magazine
1921
Earlier work this paper cites.
J. M. Keynes (1921), A Treatise on Probability
1921
Earlier work this paper cites.
F. H. Knight (1921), Risk, Uncertainty and Profit
1921
Earlier work this paper cites.
F. Hausdorff (1923), Momentprobleme für ein endliches Intervall, Mathematische Zeitschrift
1923
Earlier work this paper cites.
M. Fréchet (1935), Généralisation du théoreme des probabilités totales, Fundamenta Mathematicae
1935
Earlier work this paper cites.
S. Banach (1938), Über homogene Polynome in ( L 2 L^{2} ), Studia Mathematica
1938
Earlier work this paper cites.
H. Cramér (1938), Sur un nouveau théoreme-limite de la théorie des probabilités, Actualités Scientifiques et Industrielles
1938
Earlier work this paper cites.
H. Cramér (1946), Mathematical Methods of Statistics
1946
Earlier work this paper cites.
J. A. Shohat and J. D. Tamarkin (1950), The Problem of Moments
1950
Earlier work this paper cites.
G. E. Box (1953), Non-normality and tests on variances, Biometrika
1953
Earlier work this paper cites.
V. DeMiguel, L. Garlappi and R. Uppal (2009), Optimal versus naive diversification: How inefficient is the 1 / n 1/n portfolio strategy?, The Review of Financial Studies
1953
Earlier work this paper cites.
W. Fenchel (1953), Convex Cones, Sets, and Functions
1953
Earlier work this paper cites.
E. M. L. Beale (1955), On minimizing a convex function subject to linear inequalities, Journal of the Royal Statistical Society: Series B
1955
Earlier work this paper cites.
G. B. Dantzig (1955), Linear programming under uncertainty, Management Science
1955
Earlier work this paper cites.
D. N. Lal (1955), A note on a form of Tchebycheff’s inequality for two or more variables, Sankhyā: The Indian Journal of Statistics
1955
Earlier work this paper cites.
G. B. Dantzig (1956), The Simplex Method
1956
Earlier work this paper cites.
H. Edmundson (1956), Bounds on the expectation of a convex function of a random variable, Technical report, The Rand Corporation Paper 982, Santa Monica, California
1956
Earlier work this paper cites.
H. Richter (1957), Parameterfreie Abschätzung und Realisierung von Erwartungswerten, Blätter der DGVFM
1957
Earlier work this paper cites.
W. W. Rogosinski (1958), Moments of non-negative mass, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences
1958
Earlier work this paper cites.
H. Scarf (1958), A min-max solution to an inventory problem, in Studies in Mathematical Theory of Inventory and Production
1958
Earlier work this paper cites.
M. Sion (1958), On general minimax theorems, Pacific Journal of Mathematics
1958
Earlier work this paper cites.
S. Kullback (1959), Information theory and statistics, Wiley
1959
Earlier work this paper cites.
A. Madansky (1959), Bounds on the expectation of a convex function of a multivariate random variable, The Annals of Mathematical Statistics
1959
Earlier work this paper cites.
K. Isii (1960), The extrema of probability determined by generalized moments (I) Bounded random variables, Annals of the Institute of Statistical Mathematics
1960
Earlier work this paper cites.
J. E. Kelley, Jr (1960), The cutting-plane method for solving convex programs, Journal of the Society for Industrial and Applied Mathematics
1960
Earlier work this paper cites.
A. W. Marshall and I. Olkin (1960), A one-sided inequality of the Chebyshev type, The Annals of Mathematical Statistics
1960
Earlier work this paper cites.
D. Ellsberg (1961), Risk, ambiguity, and the Savage axioms, Quarterly Journal of Economics
1961
Earlier work this paper cites.
K. Isii (1962), On sharpness of Tchebycheff-type inequalities, Annals of the Institute of Statistical Mathematics
1962
Earlier work this paper cites.
C. Berge (1963), Topological Spaces: Including a Treatment of Multi-Valued Functions, Vector Spaces, and Convexity
1963
Earlier work this paper cites.
I. Csiszár (1963), Eine informationstheoretische Ungleichung und ihre Anwendung auf den Beweis der Ergodizität von Markoffschen Ketten, Publications of the Mathematical Institute of the Hungarian Academy of Sciences
1963
Earlier work this paper cites.
P. J. Huber (1964), Robust estimation of a location parameter, The Annals of Mathematical Statistics
1964
Earlier work this paper cites.
R. R. Phelps (1965), Lectures on Choquet’s Theorem
1965
Earlier work this paper cites.
V. Strassen (1965), The existence of probability measures with given marginals, The Annals of Mathematical Statistics
1965
Earlier work this paper cites.
S. M. Ali and S. D. Silvey (1966), A general class of coefficients of divergence of one distribution from another, Journal of the Royal Statistical Society: Series B
1966
Earlier work this paper cites.
J. Dupačová (1966), On minimax solutions of stochastic linear programming problems, Časopis pro pěstování matematiky
1966
Earlier work this paper cites.
S. Karlin and W. J. Studden (1966), Tchebycheff Systems: With Applications in Analysis and Statistics
1966
Earlier work this paper cites.
E. S. Levitin and B. T. Polyak (1966), Constrained minimization methods, USSR Computational Mathematics and Mathematical Physics
1966
Earlier work this paper cites.
S. S. Varadhan (1966), Asymptotic probabilities and differential equations, Communications on Pure and Applied Mathematics
1966
Earlier work this paper cites.
G. Zames (1966), Robust control theory, Proceedings of the IEEE
1966
Earlier work this paper cites.
I. Csiszár (1967), Information-type measures of difference of probability distributions and indirect observation, Studia Scientiarum Mathematicarum Hungarica
1967
Earlier work this paper cites.
P. J. Huber (1967), The behavior of maximum likelihood estimates under nonstandard conditions, in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability
1967
Earlier work this paper cites.
F. R. Hampel (1968), Contributions to the theory of robust estimation, Technical report, University of California, Berkeley
1968
Earlier work this paper cites.
P. J. Huber (1968), Robust confidence limits, Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete
1968
Earlier work this paper cites.
R. M. Dudley (1969), The speed of mean Glivenko-Cantelli convergence, The Annals of Mathematical Statistics
1969
Earlier work this paper cites.
R. T. Rockafellar (1970), Convex Analysis
1970
Earlier work this paper cites.
F. R. Hampel (1971), A general qualitative definition of robustness, The Annals of Mathematical Statistics
1971
Earlier work this paper cites.
V. A. Yakubovich (1971), S-procedure in nonlinear control theory, Vestnik Leninggradskogo Universiteta (in Russian)
1971
Earlier work this paper cites.
T. Başar and M. Mintz (1972), Minimax terminal state estimation for linear plants with unknown forcing functions, International Journal of Control
1972
Earlier work this paper cites.
A. Ben-Tal and E. Hochman (1972), More bounds on the expectation of a convex function of a random variable, Journal of Applied Probability
1972
Earlier work this paper cites.
T. Başar and M. Max (1973), A multistage pursuit-evasion game that admits a Gaussian random process as a maximin control policy, Stochastics
1973
Earlier work this paper cites.
T. Başar and M. Mintz (1973), On a minimax estimate for the mean of a normal random vector under a generalized quadratic loss function, The Annals of Statistics
1973
Earlier work this paper cites.
A. L. Soyster (1973), Convex programming with set-inclusive constraints and applications to inexact linear programming, Operations Research
1973
Earlier work this paper cites.
R. T. Rockafellar (1974), Conjugate Duality and Optimization
1974
Earlier work this paper cites.
T. Başar (1977), Optimum Fisherian information for multivariate distributions, The Annals of Statistics
1977
Earlier work this paper cites.
R. Jagannathan (1977), Minimax procedure for a class of linear programs under uncertainty, Operations Research
1977
Earlier work this paper cites.
G. E. Box (1979), Robustness in the strategy of scientific model building, in Robustness in statistics
1979
Earlier work this paper cites.
L. G. Khachiyan (1979), A polynomial algorithm in linear programming, Doklady Akademii Nauk
1979
Earlier work this paper cites.
P. Honeyman, R. E. Ladner and M. Yannakakis (1980), Testing the universal instance assumption, Information Processing Letters
1980
Earlier work this paper cites.
A. H. Tchen (1980), Inequalities for distributions with given marginals, The Annals of Probability
1980
Earlier work this paper cites.
Y. L. Tong (1980), Probability Inequalities in Multivariate Distributions
1980
Earlier work this paper cites.
C. Atkinson and A. F. Mitchell (1981), Rao’s distance measure, Sankhyā: The Indian Journal of Statistics, Series A
1981
Earlier work this paper cites.
P. Huber (1981), Robust Statistics
1981
Earlier work this paper cites.
T. Ü. Başar and T. Başar (1982), Optimum coding and decoding schemes for the transmission of a stochastic process over a continuous-time stochastic channel with partially unknown statisticst, Stochastics
1982
Earlier work this paper cites.
D. Dowson and B. Landau (1982), The Fréchet distance between multivariate normal distributions, Journal of Multivariate Analysis
1982
Earlier work this paper cites.
J. Hartung (1982), An extension of Sion’s minimax theorem with an application to a method for constrained games, Pacific Journal of Mathematics
1982
Earlier work this paper cites.
I. Olkin and F. Pukelsheim (1982), The distance between two random vectors with given dispersion matrices, Linear Algebra and its Applications
1982
Earlier work this paper cites.
T. Başar (1983), The Gaussian test channel with an intelligent jammer, IEEE Transactions on Information Theory
1983
Earlier work this paper cites.
M. D. Donsker and S. S. Varadhan (1983), Asymptotic evaluation of certain Markov process expectations for large time. IV, Communications on Pure and Applied Mathematics
1983
Earlier work this paper cites.
L. Rüschendorf (1983), Solution of a statistical optimization problem by rearrangement methods, Metrika
1983
Earlier work this paper cites.
M. Ajtai, J. Komlós and G. Tusnády (1984), On optimal matchings, Combinatorica
1984
Earlier work this paper cites.
T. Başar and T. Ü. Başar (1984), A bandwidth expanding scheme for communication channels with noiseless feedback in the presence of unknown jamming noise, Journal of the Franklin Institute
1984
Earlier work this paper cites.
C. Givens and R. Shortt (1984), A class of Wasserstein metrics for probability distributions, The Michigan Mathematical Journal
1984
Earlier work this paper cites.
N. Karmarkar (1984), A new polynomial-time algorithm for linear programming, Combinatorica
1984
Earlier work this paper cites.
T. Başar and Y. W. Wu (1985), A complete characterization of minimax and maximin encoder-decoder policies for communication channels with incomplete statistical description, IEEE Transactions on Information Theory
1985
Earlier work this paper cites.
Y. Ermoliev, A. A. Gaivoronski and C. Nedeva (1985), Stochastic optimization problems with incomplete information on distribution functions, SIAM Journal on Control and Optimization
1985
Earlier work this paper cites.
R. A. Horn and C. R. Johnson (1985), H ∞ \infty -optimal control and related minimax design problems, IEEE Transactions on Automatic Control
1985
Earlier work this paper cites.
T. Başar and Y. W. Wu (1986), Solutions to a class of minimax decision problems arising in communication systems, Journal of Optimization Theory and Applications
1986
Earlier work this paper cites.
A. Ben-Tal and M. Teboulle (1986), Expected utility, penalty functions, and duality in stochastic nonlinear programming, Management Science
1986
Earlier work this paper cites.
J. Birge and R.-B. Wets (1986), Designing approximation schemes for stochastic optimization problems, in particular for stochastic programs with recourse, Mathematical Programming Study
1986
Earlier work this paper cites.
H. Gassmann and W. Ziemba (1986), A tight upper bound for the expectation of a convex function of a multivariate random variable, in Stochastic Programming 84 Part I
1986
Earlier work this paper cites.
K. Marton (1986), A simple proof of the blowing-up lemma, IEEE Transactions on Information Theory
1986
Earlier work this paper cites.
J. Dupačová (1987), The minimax approach to stochastic programming and an illustrative application, Stochastics
1987
Earlier work this paper cites.
F. Liese and I. Vajda (1987), Convex Statistical Distances
1987
Earlier work this paper cites.
S. Dharmadhikari and K. Joag-Dev (1988), Unimodality, Convexity, and Applications
1988
Earlier work this paper cites.
D. L. Donoho and R. C. Liu (1988), The "automatic" robustness of minimum distance functionals, The Annals of Statistics
1988
Earlier work this paper cites.
J. Dupacová and R. Wets (1988), Asymptotic behavior of statistical estimators and of optimal solutions of stochastic optimization problems, The Annals of Statistics
1988
Earlier work this paper cites.
G. Georgakopoulos, D. Kavvadias and C. H. Papadimitriou (1988), Probabilistic satisfiability, Journal of Complexity
1988
Earlier work this paper cites.
A. B. Owen (1988), Empirical likelihood ratio confidence intervals for a single functional, Biometrika
1988
Earlier work this paper cites.
J. C. Doyle, K. Glover, P. Khargonekar and B. Francis (1989), Robust control of time-delay systems, IEEE Transactions on Automatic Control
1989
Earlier work this paper cites.
I. Gilboa and D. Schmeidler (1989), Maxmin expected utility with a non-unique prior, Journal of Mathematical Economics
1989
Earlier work this paper cites.
R. O. Michaud (1989), The Markowitz optimization enigma: Is ‘optimized’ optimal?, Financial Analysts Journal
1989
Earlier work this paper cites.
A. Shapiro (1989), Asymptotic properties of statistical estimators in stochastic programming, The Annals of Statistics
1989
Earlier work this paper cites.
M. Gelbrich (1990), On a formula for the L 2 {L}^{2} Wasserstein metric between measures on Euclidean and Hilbert spaces, Mathematische Nachrichten
1990
Earlier work this paper cites.
A. B. Owen (1990), Empirical likelihood ratio confidence regions, The Annals of Statistics
1990
Earlier work this paper cites.
A. Shapiro (1990), On differential stability in stochastic programming, Mathematical Programming
1990
Earlier work this paper cites.
P. Whittle (1990), Risk-Sensitive Optimal Control
1990
Earlier work this paper cites.
A. Ben-Tal, A. Ben-Israel and M. Teboulle (1991), Certainty equivalents and information measures: duality and extremal principles, Journal of Mathematical Analysis and Applications
1991
Earlier work this paper cites.
Y. Brenier (1991), Polar factorization and monotone rearrangement of vector-valued functions, Communications on Pure and Applied Mathematics
1991
Earlier work this paper cites.
A. A. Gaivoronski (1991), A numerical method for solving stochastic programming problems with moment constraints on a distribution function, Annals of Operations Research
1991
Earlier work this paper cites.
A. J. King and R. J.-B. Wets (1991), Epi-consistency of convex stochastic programs, Stochastics and Stochastic Reports
1991
Earlier work this paper cites.
A. B. Owen (1991), Empirical likelihood for linear models, The Annals of Statistics
1991
Earlier work this paper cites.
L. Rüschendorf (1991), Fréchet-bounds and their applications, in Advances in Probability Distributions with Given Marginals: Beyond the Copulas
1991
Earlier work this paper cites.
A. Shapiro (1991), Asymptotic analysis of stochastic programs, Annals of Operations Research
1991
Earlier work this paper cites.
O. Zeitouni and M. Gutman (1991), On universal hypotheses testing via large deviations, IEEE Transactions on Information Theory
1991
Earlier work this paper cites.
J. H. Dulá and R. V. Murthy (1992), A Tchebysheff-type bound on the expectation of sublinear polyhedral functions, Operations Research
1992
Earlier work this paper cites.
K. Frauendorfer (1992), Stochastic Two-Stage Programming
1992
Earlier work this paper cites.
G. Gallego and I. Moon (1993), The distribution free newsboy problem: Review and extensions, The Journal of the Operational Research Society
1993
Earlier work this paper cites.
A. J. King and R. T. Rockafellar (1993), Asymptotic theory for solutions in statistical estimation and stochastic programming, Mathematics of Operations Research
1993
Earlier work this paper cites.
A. Shapiro (1993), Asymptotic behavior of optimal solutions in stochastic programming, Mathematics of Operations Research
1993
Earlier work this paper cites.
J. Dupačová (1994), Applications of stochastic programming under incomplete information, Journal of Computational and Applied Mathematics
1994
Earlier work this paper cites.
Y. Nesterov and A. Nemirovskii (1994), Interior-Point Polynomial Algorithms in Convex Programming
1994
Earlier work this paper cites.
T. Başar and P. Bernhard (1995), ℋ ∞ \mathcal{H}_{\infty} -optimal Control and Related Minimax Design Problems: A Dynamic Game Approach
1995
Earlier work this paper cites.
V. Dobrić and J. E. Yukich (1995), Asymptotics for transportation cost in high dimensions, Journal of Theoretical Probability
1995
Earlier work this paper cites.
M. Green and D. J. N. Limebeer (1995), H-infinity control theory: A tutorial, Automatica
1995
Earlier work this paper cites.
A. A. Salo and M. Weber (1995), Ambiguity aversion in first-price sealed-bid auctions, Journal of Risk and Uncertainty
1995
Earlier work this paper cites.
H. K. Khalil (1996), Control System Analysis and Design with Advanced Design Tools
1996
Earlier work this paper cites.
M. Talagrand (1996), Transportation cost for Gaussian and other product measures, Geometric & Functional Analysis
1996
Earlier work this paper cites.
K. Zhou, J. C. Doyle and K. Glover (1996), Robust and Optimal Control
1996
Earlier work this paper cites.
O. Kallenberg (1997), Foundations of Modern Probability
1997
Earlier work this paper cites.
P. Kouvelis and G. Yu (1997), Robust Discrete Optimization and Its Applications
1997
Earlier work this paper cites.
S. Peng (1997), Backward SDE and related G-expectation, in Backward Stochastic Differential Equations in Finance
1997
Earlier work this paper cites.
A. Ben-Tal and A. Nemirovski (1998), Robust convex optimization, Mathematics of Operations Research
1998
Earlier work this paper cites.
L. El Ghaoui and H. Lebret (1998 a
1998
Earlier work this paper cites.
L. El Ghaoui and H. Lebret (1998 b
1998
Earlier work this paper cites.
L. El Ghaoui, F. Oustry and H. Lebret (1998), Robust solutions to uncertain semidefinite programs, SIAM Journal on Optimization
1998
Earlier work this paper cites.
A. W. Van der Vaart (1998), Asymptotic Statistics
1998
Earlier work this paper cites.
M. Anthony and P. L. Bartlett (1999), Neural Network Learning: Theoretical Foundations
1999
Earlier work this paper cites.
P. Artzner, F. Delbaen, J.-M. Eber and D. Heath (1999), Coherent measures of risk, Mathematical Finance
1999
Earlier work this paper cites.
A. Ben-Tal and A. Nemirovski (1999 a
1999
Earlier work this paper cites.
A. Ben-Tal and A. Nemirovski (1999 b
1999
Earlier work this paper cites.
G. B. Folland (1999), Real Analysis: Modern Techniques and Their Applications
1999
Earlier work this paper cites.
T. Sutter, B. P. Van Parys and D. Kuhn (2024), A Pareto dominance principle for data-driven optimization, Operations Research
1999
Earlier work this paper cites.
K. Zhou and J. C. Doyle (1999), Essentials of Robust Control
1999
Earlier work this paper cites.
A. Ben-Tal and A. Nemirovski (2000), Robust solutions of linear programming problems contaminated with uncertain data, Mathematical Programming
2000
Earlier work this paper cites.
D. Bertsimas and J. Sethuraman (2000), Moment problems and semidefinite optimization, in Handbook of Semidefinite Programming: Theory, Algorithms, and Applications
2000
Earlier work this paper cites.
J. R. Munkres (2000), Topology
2000
Earlier work this paper cites.
P. A. Parrilo (2000), Structured Semidefinite Programs and Semialgebraic Geometry Methods in Robustness and Optimization, PhD thesis, California Institute of Technology
2000
Earlier work this paper cites.
R. T. Rockafellar and S. Uryasev (2000), Optimization of conditional value-at-risk, Journal of Risk
2000
Earlier work this paper cites.
A. Van Der Vaart and J. A. Wellner (2000), Preservation theorems for Glivenko-Cantelli and uniform Glivenko-Cantelli classes, in High Dimensional Probability II
2000
Earlier work this paper cites.
A. Ben-Tal and A. Nemirovski (2001), Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications
2001
Earlier work this paper cites.
G. E. Dullerud and F. Paganini (2001), A Course in Robust Control Theory: A Convex Approach
2001
Earlier work this paper cites.
S. Kusuoka (2001), On law invariant coherent risk measures, in Advances in Mathematical Economics
2001
Earlier work this paper cites.
G. R. Lanckriet, L. El Ghaoui, C. Bhattacharyya and M. I. Jordan (2001), Minimax probability machine, in Advances in Neural Information Processing Systems
2001
Earlier work this paper cites.
J. B. Lasserre (2001), Global optimization with polynomials and the problem of moments, SIAM Journal on Optimization
2001
Earlier work this paper cites.
A. B. Owen (2001), Empirical Likelihood
2001
Earlier work this paper cites.
A. Shapiro (2001), On duality theory of conic linear problems, in Semi-Infinite Programming
2001
Earlier work this paper cites.
C. Acerbi (2002), Spectral measures of risk: A coherent representation of subjective risk aversion, Journal of Banking & Finance
2002
Earlier work this paper cites.
A. Ben-Tal and A. Nemirovski (2002), Robust optimization–methodology and applications, Mathematical Programming
2002
Earlier work this paper cites.
D. Bertsimas and I. Popescu (2002), On the relation between option and stock prices: A convex optimization approach, Operations Research
2002
Earlier work this paper cites.
F. Delbaen (2002), Coherent risk measures on general probability spaces, in Advances in Finance and Stochastics: Essays in Honour of Dieter Sondermann
2002
Earlier work this paper cites.
S. G. Krantz and H. R. Parks (2002), A Primer of Real Analytic Functions
2002
Earlier work this paper cites.
G. R. Lanckriet, L. El Ghaoui, C. Bhattacharyya and M. I. Jordan (2002), A robust minimax approach to classification, Journal of Machine Learning Research
2002
Earlier work this paper cites.
J. B. Lasserre (2002), Bounds on measures satisfying moment conditions, The Annals of Applied Probability
2002
Earlier work this paper cites.
R. T. Rockafellar and S. Uryasev (2002), Conditional value-at-risk for general loss distributions, Journal of Banking & Finance
2002
Earlier work this paper cites.
A. Shapiro and A. Kleywegt (2002), Minimax analysis of stochastic problems, Optimization Methods and Software
2002
Earlier work this paper cites.
T. Strohmann and G. Z. Grudic (2002), A formulation for minimax probability machine regression, in Advances in Neural Information Processing Systems
2002
Earlier work this paper cites.
L. El Ghaoui, M. Oks and F. Oustry (2003), Worst-case value-at-risk and robust portfolio optimization: A conic programming approach, Operations Research
2003
Earlier work this paper cites.
L. G. Epstein and J. Miao (2003), A two-person dynamic equilibrium under ambiguity, Journal of Economic Dynamics and Control
2003
Earlier work this paper cites.
S. Mendelson (2003), A few notes on statistical learning theory, in Advanced Lectures on Machine Learning
2003
Earlier work this paper cites.
P. A. Parrilo (2003), Semidefinite programming relaxations for semialgebraic problems, Mathematical Programming
2003
Earlier work this paper cites.
A. Shapiro (2003), Monte Carlo sampling methods, in Stochastic Programming
2003
Earlier work this paper cites.
C. Villani (2003), Topics in Optimal Transportation
2003
Earlier work this paper cites.
D. Bertsimas and M. Sim (2004), The price of robustness, Operations Research
2004
Earlier work this paper cites.
D. Bertsimas, K. Natarajan and C.-P. Teo (2004), Probabilistic combinatorial optimization: Moments, semidefinite programming, and asymptotic bounds, SIAM Journal on Optimization
2004
Earlier work this paper cites.
C. Bhattacharyya (2004), Second order cone programming formulations for feature selection, Journal of Machine Learning Research
2004
Earlier work this paper cites.
O. Bousquet, S. Boucheron and G. Lugosi (2004), Introduction to statistical learning theory, in Advanced Lectures on Machine Learning
2004
Earlier work this paper cites.
D. P. De Farias and B. Van Roy (2004), On constraint sampling in the linear programming approach to approximate dynamic programming, Mathematics of Operations Research
2004
Earlier work this paper cites.
K. Huang, H. Yang, I. King, M. R. Lyu and L. Chan (2004), The minimum error minimax probability machine, Journal of Machine Learning Research
2004
Earlier work this paper cites.
B. C. Levy and R. Nikoukhah (2004), Robust least-squares estimation with a relative entropy constraint, IEEE Transactions on Information Theory
2004
Earlier work this paper cites.
K. G. Nishimura and H. Ozaki (2004), Search and Knightian uncertainty, Journal of Economic Theory
2004
Earlier work this paper cites.
D. Bertsimas and I. Popescu (2005), Optimal inequalities in probability theory: A convex optimization approach, SIAM Journal on Optimization
2005
Earlier work this paper cites.
G. C. Calafiore and M. C. Campi (2005), Uncertain convex programs: Randomized solutions and confidence levels, Mathematical Programming
2005
Earlier work this paper cites.
S. P. Dokov and D. P. Morton (2005), Second-order lower bounds on the expectation of a convex function, Mathematics of Operations Research
2005
Earlier work this paper cites.
M. Hsu, M. Bhatt, R. Adolphs, D. Tranel and C. F. Camerer (2005), Neural systems responding to degrees of uncertainty in human decision-making, Science
2005
Earlier work this paper cites.
D. Kuhn (2005), Generalized Bounds for Convex Multistage Stochastic Programs
2005
Earlier work this paper cites.
I. Popescu (2005), A semidefinite programming approach to optimal-moment bounds for convex classes of distributions, Mathematics of Operations Research
2005
Earlier work this paper cites.
L. F. Zuluaga and J. F. Pena (2005), A conic programming approach to generalized Tchebycheff inequalities, Mathematics of Operations Research
2005
Earlier work this paper cites.
S. Ahmed (2006), Convexity and decomposition of mean-risk stochastic programs, Mathematical Programming
2006
Earlier work this paper cites.
D. Bertsimas, K. Natarajan and C.-P. Teo (2006 a
2006
Earlier work this paper cites.
D. Bertsimas, K. Natarajan and C.-P. Teo (2006 b
2006
Earlier work this paper cites.
C. M. Bishop (2006), Pattern Recognition and Machine Learning
2006
Earlier work this paper cites.
G. C. Calafiore and M. C. Campi (2006), The scenario approach to robust control design, IEEE Transactions on Automatic Control
2006
Earlier work this paper cites.
G. C. Calafiore and L. El Ghaoui (2006), On distributionally robust chance-constrained linear programs, Journal of Optimization Theory and Applications
2006
Earlier work this paper cites.
T. Cover and J. Thomas (2006), Elements of Information Theory
2006
Earlier work this paper cites.
J. Dupačová (2006), Stress testing via contamination, in Coping with Uncertainty: Modeling and Policy Issues
2006
Earlier work this paper cites.
M. Dyer and L. Stougie (2006), Computational complexity of stochastic programming problems, Mathematical Programming
2006
Earlier work this paper cites.
P. Embrechts and G. Puccetti (2006), Bounds for functions of multivariate risks, Journal of Multivariate Analysis
2006
Earlier work this paper cites.
E. Erdoğan and G. Iyengar (2006), Ambiguous chance constrained problems and robust optimization, Mathematical Programming
2006
Earlier work this paper cites.
A. L. Krain, A. M. Wilson, R. Arbuckle, X. F. Castellanos and M. P. Milham (2006), Distinct neural mechanisms of risk and ambiguity: A meta-analysis of decision-making, NeuroImage
2006
Earlier work this paper cites.
E. L. Lehmann and G. Casella (2006), Theory of Point Estimation
2006
Earlier work this paper cites.
K. G. Nishimura and H. Ozaki (2006), An axiomatic approach to-contamination, Economic Theory
2006
Earlier work this paper cites.
R. T. Rockafellar, S. Uryasev and M. Zabarankin (2006), Generalized deviations in risk analysis, Finance and Stochastics
2006
Earlier work this paper cites.
A. Ruszczyński and A. Shapiro (2006), Optimization of convex risk functions, Mathematics of Operations Research
2006
Earlier work this paper cites.
S. L. Savage, S. Scholtes and D. Zweidler (2006), Probability management, OR/MS Today
2006
Earlier work this paper cites.
J. E. Smith and R. L. Winkler (2006), The optimizer’s curse: Skepticism and postdecision surprise in decision analysis, Management Science
2006
Earlier work this paper cites.
J. Yue, B. Chen and M.-C. Wang (2006), Expected value of distribution information for the newsvendor problem, Operations Research
2006
Earlier work this paper cites.
A. Ben-Tal and M. Teboulle (2007), An old-new concept of convex risk measures: The optimized certainty equivalent, Mathematical Finance
2007
Earlier work this paper cites.
F. Bolley, A. Guillin and C. Villani (2007), Quantitative concentration inequalities for empirical measures on non-compact spaces, Probability Theory and Related Fields
2007
Earlier work this paper cites.
L. Cabantous (2007), Ambiguity aversion in the field of insurance: Insurers’ attitude to imprecise and conflicting probability estimates, Theory and Decision
2007
Earlier work this paper cites.
G. C. Calafiore (2007), Ambiguous risk measures and optimal robust portfolios, SIAM Journal on Optimization
2007
Earlier work this paper cites.
R. S. Ellis (2007), Entropy, Large Deviations, and Statistical Mechanics
2007
Earlier work this paper cites.
S. L. Janak, X. Lin and C. A. Floudas (2007), A new robust optimization approach for scheduling under uncertainty: II. Uncertainty with known probability distribution, Computers & Chemical Engineering
2007
Earlier work this paper cites.
K. Natarajan and Z. Linyi (2007), A mean–variance bound for a three-piece linear function, Probability in the Engineering and Informational Sciences
2007
Earlier work this paper cites.
A. Nemirovski and A. Shapiro (2007), Convex approximations of chance constrained programs, SIAM Journal on Optimization
2007
Earlier work this paper cites.
2007
Earlier work this paper cites.
S. Peng (2007 b
2007
Earlier work this paper cites.
G. C. Pflug and D. Wozabal (2007), Ambiguity in portfolio selection, Quantitative Finance
2007
Earlier work this paper cites.
I. Pólik and T. Terlaky (2007), A survey of the S-lemma, SIAM Review
2007
Earlier work this paper cites.
I. Popescu (2007), Robust mean-covariance solutions for stochastic optimization, Operations Research
2007
Earlier work this paper cites.
L. Ambrosio, N. Gigli and G. Savaré (2008), Gradient Flows: In Metric Spaces and in the Space of Probability Measures
2008
Earlier work this paper cites.
D. Bergemann and K. H. Schlag (2008), Pricing without priors, Journal of the European Economic Association
2008
Earlier work this paper cites.
M. C. Campi and S. Garatti (2008), The exact feasibility of randomized solutions of uncertain convex programs, SIAM Journal on Optimization
2008
Cited alongside, same era.
T. Champion, L. De Pascale and P. Juutinen (2008), The ∞ \infty -Wasserstein distance: Local solutions and existence of optimal transport maps, SIAM Journal on Mathematical Analysis
2008
Cited alongside, same era.
P. Clément and W. Desch (2008), Wasserstein metric and subordination, Studia Mathematica
2008
Cited alongside, same era.
H. Föllmer and A. Schied (2008), Stochastic Finance. An Introduction in Discrete Time
2008
Cited alongside, same era.
L. P. Hansen and T. J. Sargent (2008), Robustness
2008
Cited alongside, same era.
H. Husain (2020), Distributional robustness with IPMs and links to regularization and GANs, in Advances in Neural Information Processing Systems
2020
Later among the works it cites.
Ç. Koçyiğit, G. Iyengar, D. Kuhn and W. Wiesemann (2020), Distributionally robust mechanism design, Management Science
2020
Later among the works it cites.
Y. Kwon, W. Kim, J.-H. Won and M. C. Paik (2020), Principled learning method for Wasserstein distributionally robust optimization with local perturbations, in International Conference on Machine Learning
2020
Later among the works it cites.
J. Lee, S. Park and J. Shin (2020), Learning bounds for risk-sensitive learning, in Advances in Neural Information Processing Systems
2020
Later among the works it cites.
D. Levy, Y. Carmon, J. C. Duchi and A. Sidford (2020), Large-scale methods for distributionally robust optimization, in Advances in Neural Information Processing Systems
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2008
Cited alongside, same era.
B. C. Levy (2008), Robust hypothesis testing with a relative entropy tolerance, IEEE Transactions on Information Theory
2008
Cited alongside, same era.
G. Perakis and G. Roels (2008), Regret in the newsvendor model with partial information, Operations Research
2008
Cited alongside, same era.
R. T. Rockafellar, S. Uryasev and M. Zabarankin (2008), Risk tuning with generalized linear regression, Mathematics of Operations Research
2008
Cited alongside, same era.
C. Villani (2008), Optimal Transport: Old and New
2008
Cited alongside, same era.
A. Beck and A. Ben-Tal (2009), Duality in robust optimization: Primal worst equals dual best, Operations Research Letters
2009
Cited alongside, same era.
A. Ben-Tal, L. El Ghaoui and A. Nemirovski (2009), Robust Optimization
2009
Cited alongside, same era.
2020
Later among the works it cites.
D. Li and S. Martínez (2020), Data assimilation and online optimization with performance guarantees, IEEE Transactions on Automatic Control
2020
Later among the works it cites.
J. Li, C. Chen and A. M.-C. So (2020), Fast epigraphical projection-based incremental algorithms for Wasserstein distributionally robust support vector machine, in Advances in Neural Information Processing Systems
2020
Later among the works it cites.
V. A. Nguyen, F. Zhang, J. Blanchet, E. Delage and Y. Ye (2020), Distributionally robust local non-parametric conditional estimation, in Advances in Neural Information Processing Systems
2020
Later among the works it cites.
V. M. Panaretos and Y. Zemel (2020), An Invitation to Statistics in Wasserstein Space
2020
Later among the works it cites.
N. Rontsis, M. A. Osborne and P. J. Goulart (2020), Distributionally ambiguous optimization for batch Bayesian optimization, Journal of Machine Learning Research
2020
Later among the works it cites.
S. Sagawa, P. W. Koh, T. B. Hashimoto and P. Liang (2020), Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization, in International Conference on Learning Representations
2020
Later among the works it cites.
K. S. Shehadeh, A. E. Cohn and R. Jiang (2020), A distributionally robust optimization approach for outpatient colonoscopy scheduling, European Journal of Operational Research
2020
Later among the works it cites.
S. Wang, Z. Chen and T. Liu (2020), Distributionally robust hub location, Transportation Science
2020
Later among the works it cites.
W. Xie (2020), Tractable reformulations of distributionally robust two-stage stochastic programs over the type- ∞ \infty Wasserstein ball, Operations Research Letters
2020
Later among the works it cites.
I. Yang (2020), Wasserstein distributionally robust stochastic control: A data-driven approach, IEEE Transactions on Automatic Control
2020
Later among the works it cites.
A. Y. Zhang and H. H. Zhou (2020), Theoretical and computational guarantees of mean field variational inference for community detection, The Annals of Statistics
2020
Later among the works it cites.
J.-J. Zhu, W. Jitkrittum, M. Diehl and B. Schölkopf (2020), Worst-case risk quantification under distributional ambiguity using kernel mean embedding in moment problem, in IEEE Conference on Decision and Control
2020
Later among the works it cites.
Y. An and R. Gao (2021), Generalization bounds for (Wasserstein) robust optimization, in Advances in Neural Information Processing Systems
2021
Later among the works it cites.
D. Bartl, S. Drapeau, J. Oblój and J. Wiesel (2021), Sensitivity analysis of Wasserstein distributionally robust optimization problems, Proceedings of the Royal Society A
2021
Later among the works it cites.
2021
Later among the works it cites.
D. Bertsimas, D. den Hertog and J. Pauphilet (2021), Probabilistic guarantees in robust optimization, SIAM Journal on Optimization
2021
Later among the works it cites.
J. Blanchet and Y. Kang (2021), Sample out-of-sample inference based on Wasserstein distance, Operations Research
2021
Later among the works it cites.
J. Blanchet, K. Murthy and V. A. Nguyen (2021), Statistical analysis of Wasserstein distributionally robust estimators, INFORMS Tutorials in Operations Research
2021
Later among the works it cites.
J. Coulson, J. Lygeros and F. Dörfler (2021), Distributionally robust chance constrained data-enabled predictive control, IEEE Transactions on Automatic Control
2021
Later among the works it cites.
B. Das, A. Dhara and K. Natarajan (2021), On the heavy-tail behavior of the distributionally robust newsvendor, Operations Research
2021
Later among the works it cites.
J. C. Duchi and H. Namkoong (2021), Learning models with uniform performance via distributionally robust optimization, The Annals of Statistics
2021
Later among the works it cites.
J. C. Duchi, P. W. Glynn and H. Namkoong (2021), Statistics of robust optimization: A generalized empirical likelihood approach, Mathematics of Operations Research
2021
Later among the works it cites.
C. Finlay and A. M. Oberman (2021), Scaleable input gradient regularization for adversarial robustness, Machine Learning with Applications
2021
Later among the works it cites.
S. Ghosh, M. Squillante and E. Wollega (2021), Efficient stochastic gradient descent for learning with distributionally robust optimization, in Advances in Neural Information Processing Systems
2021
Later among the works it cites.
J.-y. Gotoh, M. J. Kim and A. E. Lim (2021), Calibration of distributionally robust empirical optimization models, Operations Research
2021
Later among the works it cites.
B. Han, C. Shang and D. Huang (2021), Multiple kernel learning-aided robust optimization: Learning algorithm, computational tractability, and usage in multi-stage decision-making, European Journal of Operational Research
2021
Later among the works it cites.
2021
Later among the works it cites.
L. J. Hong, Z. Huang and H. Lam (2021), Learning-based robust optimization: Procedures and statistical guarantees, Management Science
2021
Later among the works it cites.
Y. Hu, X. Chen and N. He (2021), On the bias-variance-cost tradeoff of stochastic optimization, in Advances in Neural Information Processing Systems
2021
Later among the works it cites.
W. Jongeneel, T. Sutter and D. Kuhn (2021), Topological linear system identification via moderate deviations theory, IEEE Control Systems Letters
2021
Later among the works it cites.
C. Kent, J. Li, J. Blanchet and P. W. Glynn (2021), Modified Frank Wolfe in probability space, in Advances in Neural Information Processing Systems
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
J. B. Lasserre and T. Weisser (2021), Distributionally robust polynomial chance-constraints under mixture ambiguity sets, Mathematical Programming
2021
Later among the works it cites.
M. Li, T. Sutter and D. Kuhn (2021), Distributionally robust optimization with Markovian data, in International Conference on Machine Learning
2021
Later among the works it cites.
H. Nakao, R. Jiang and S. Shen (2021), Distributionally robust partially observable Markov decision process with moment-based ambiguity, SIAM Journal on Optimization
2021
Later among the works it cites.
K. Natarajan (2021), Optimization with Marginals and Moments
2021
Later among the works it cites.
2021
Later among the works it cites.
C. Ordoudis, V. A. Nguyen, D. Kuhn and P. Pinson (2021), Energy and reserve dispatch with distributionally robust joint chance constraints, Operations Research Letters
2021
Later among the works it cites.
M. S. Pydi and V. Jog (2021), Adversarial risk via optimal transport and optimal couplings, IEEE Transactions on Information Theory
2021
Later among the works it cites.
A. Ruszczyński (2021), A stochastic subgradient method for nonsmooth nonconvex multilevel composition optimization, SIAM Journal on Control and Optimization
2021
Later among the works it cites.
2021
Later among the works it cites.
T. Sutter, A. Krause and D. Kuhn (2021), Robust generalization despite distribution shift via minimum discriminating information, in Advances in Neural Information Processing Systems
2021
Later among the works it cites.
B. Taşkesen, M.-C. Yue, J. Blanchet, D. Kuhn and V. A. Nguyen (2021), Sequential domain adaptation by synthesizing distributionally robust experts, in International Conference on Machine Learning
2021
Later among the works it cites.
J. S. Van Leeuwaarden and C. Stegehuis (2021), Robust subgraph counting with distribution-free random graph analysis, Physical Review E
2021
Later among the works it cites.
B. P. Van Parys, P. Mohajerin Esfahani and D. Kuhn (2021), From data to decisions: Distributionally robust optimization is optimal, Management Science
2021
Later among the works it cites.
H. Vu, T. Tran, M.-C. Yue and V. A. Nguyen (2021), Distributionally robust fair principal components via geodesic descents, in International Conference on Learning Representations
2021
Later among the works it cites.
J. Wang, R. Gao and Y. Xie (2021), Sinkhorn distributionally robust optimization, arXiv:2109.11926
2021
Later among the works it cites.
S. Wu, S. Sun, J. A. Camilleri, S. B. Eickhoff and R. Yu (2021), Better the devil you know than the devil you don’t: Neural processing of risk and ambiguity, NeuroImage
2021
Later among the works it cites.
W. Xie (2021), On distributionally robust chance constrained programs with Wasserstein distance, Mathematical Programming
2021
Later among the works it cites.
L. Xin and D. A. Goldberg (2021), Time (in)consistency of multistage distributionally robust inventory models with moment constraints, European Journal of Operational Research
2021
Later among the works it cites.
J.-J. Zhu, W. Jitkrittum, M. Diehl and B. Schölkopf (2021), Kernel distributionally robust optimization: Generalized duality theorem and stochastic approximation, in International Conference on Artificial Intelligence and Statistics
2021
Later among the works it cites.
L. Aolaritei, N. Lanzetti, H. Chen and F. Dörfler (2022 a
2022
Later among the works it cites.
L. Aolaritei, S. Shafiee and F. Dörfler (2022 b
2022
Later among the works it cites.
D. Bertsimas and D. den Hertog (2022), Robust and Adaptive Optimization
2022
Later among the works it cites.
D. Bertsimas and B. P. Van Parys (2022), Bootstrap robust prescriptive analytics, Mathematical Programming
2022
Later among the works it cites.
D. Bertsimas, S. Shtern and B. Sturt (2022), Two-stage sample robust optimization, Operations Research
2022
Later among the works it cites.
J. Blanchet, L. Chen and X. Y. Zhou (2022 a
2022
Later among the works it cites.
J. Blanchet, K. Murthy and N. Si (2022 b
2022
Later among the works it cites.
J. Blanchet, K. Murthy and F. Zhang (2022 c
2022
Later among the works it cites.
J. Brugman, J. S. Van Leeuwaarden and C. Stegehuis (2022), Sharpest possible clustering bounds using robust random graph analysis, Physical Review E
2022
Later among the works it cites.
N. Bui, D. Nguyen and V. A. Nguyen (2022), Counterfactual plans under distributional ambiguity, in International Conference on Learning Representations
2022
Later among the works it cites.
Y. Carmon and D. Hausler (2022), Distributionally robust optimization via ball oracle acceleration, in Advances in Neural Information Processing Systems
2022
Later among the works it cites.
L. Chen, W. Ma, K. Natarajan, D. Simchi-Levi and Z. Yan (2022), Distributionally robust linear and discrete optimization with marginals, Operations Research
2022
Later among the works it cites.
L. Chizat (2022), Sparse optimization on measures with over-parameterized gradient descent, Mathematical Programming
2022
Later among the works it cites.
P. Dupuis and Y. Mao (2022), Formulation and properties of a divergence used to compare probability measures without absolute continuity, ESAIM: Control, Optimisation and Calculus of Variations
2022
Later among the works it cites.
A. Esteban-Pérez and J. M. Morales (2022), Distributionally robust stochastic programs with side information based on trimmings, Mathematical Programming
2022
Later among the works it cites.
C. A. García Trillos and N. García Trillos (2022), On the regularized risk of distributionally robust learning over deep neural networks, Research in the Mathematical Sciences
2022
Later among the works it cites.
N. García Trillos and R. Murray (2022), Adversarial classification: Necessary conditions and geometric flows, Journal of Machine Learning Research
2022
Later among the works it cites.
M. Gürbüzbalaban, A. Ruszczyński and L. Zhu (2022), A stochastic subgradient method for distributionally robust non-convex and non-smooth learning, Journal of Optimization Theory and Applications
2022
Later among the works it cites.
E. Hazan (2022), Introduction to Online Convex Optimization
2022
Later among the works it cites.
N. Ho-Nguyen, F. Kılınç-Karzan, S. Küçükyavuz and D. Lee (2022), Distributionally robust chance-constrained programs with right-hand side uncertainty under Wasserstein ambiguity, Mathematical Programming
2022
Later among the works it cites.
W. Jongeneel, T. Sutter and D. Kuhn (2022), Efficient learning of a linear dynamical system with stability guarantees, IEEE Transactions on Automatic Control
2022
Later among the works it cites.
Ç. Koçyiğit, N. Rujeerapaiboon and D. Kuhn (2022), Robust multidimensional pricing: Separation without regret, Mathematical Programming
2022
Later among the works it cites.
A. Kurakin, I. J. Goodfellow and S. Bengio (2022), Adversarial machine learning at scale, in International Conference on Learning Representations
2022
Later among the works it cites.
M. Lambert, S. Chewi, F. Bach, S. Bonnabel and P. Rigollet (2022), Variational inference via Wasserstein gradient flows, in Advances in Neural Information Processing Systems
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Li, S. Lin, J. Blanchet and V. A. Nguyen (2022), Tikhonov regularization is optimal transport robust under martingale constraints, in Advances in Neural Information Processing Systems
2022
Later among the works it cites.
2022
Later among the works it cites.
C. Maheshwari, C.-Y. Chiu, E. Mazumdar, S. Sastry and L. Ratliff (2022), Zeroth-order methods for convex-concave min-max problems: Applications to decision-dependent risk minimization, in International Conference on Artificial Intelligence and Statistics
2022
Later among the works it cites.
J. Milz and M. Ulbrich (2022), An approximation scheme for distributionally robust PDE-constrained optimization, SIAM Journal on Control and Optimization
2022
Later among the works it cites.
D. Nguyen, N. Bui and V. A. Nguyen (2022 a
2022
Later among the works it cites.
V. A. Nguyen, D. Kuhn and P. Mohajerin Esfahani (2022 b
2022
Later among the works it cites.
2022
Later among the works it cites.
H. Rahimian and S. Mehrotra (2022), Frameworks and results in distributionally robust optimization, Open Journal of Mathematical Optimization
2022
Later among the works it cites.
H. Rahimian, G. Bayraksan and T. Homem-de-Mello (2022), Effective scenarios in multistage distributionally robust optimization with a focus on total variation distance, SIAM Journal on Optimization
2022
Later among the works it cites.
2022
Later among the works it cites.
A. Selvi, M. R. Belbasi, M. Haugh and W. Wiesemann (2022), Wasserstein logistic regression with mixed features, in Advances in Neural Information Processing Systems
2022
Later among the works it cites.
A. Terpin, N. Lanzetti, B. Yardim, F. Dörfler and G. Ramponi (2022), Trust region policy optimization with optimal transport discrepancies: Duality and algorithm for continuous actions, in Advances in Neural Information Processing Systems
2022
Later among the works it cites.
W. J. van Eekelen, D. den Hertog and J. S. van Leeuwaarden (2022), MAD dispersion measure makes extremal queue analysis simple, INFORMS Journal on Computing
2022
Later among the works it cites.
2022
Later among the works it cites.
W. Xie, S. Ahmed and R. Jiang (2022), Optimized Bonferroni approximations of distributionally robust joint chance constraints, Mathematical Programming
2022
Later among the works it cites.
L. Xin and D. A. Goldberg (2022), Distributionally robust inventory control when demand is a martingale, Mathematics of Operations Research
2022
Later among the works it cites.
J. Yang, L. Zhang, N. Chen, R. Gao and M. Hu (2022), Decision-making with side information: A causal transport robust approach, Available from Optimization Online
2022
Later among the works it cites.
Y. Yu, T. Lin, E. V. Mazumdar and M. Jordan (2022), Fast distributionally robust learning with variance-reduced min-max optimization, in International Conference on Artificial Intelligence and Statistics
2022
Later among the works it cites.
M.-C. Yue, D. Kuhn and W. Wiesemann (2022), On linear optimization over Wasserstein balls, Mathematical Programming
2022
Later among the works it cites.
Y. Zeng and H. Lam (2022), Generalization bounds with minimal dependency on hypothesis class via distributionally robust optimization, in Advances in Neural Information Processing Systems
2022
Later among the works it cites.
B. Zhu, J. Jiao and J. Steinhardt (2022 a
2022
Later among the works it cites.
S. Zhu, L. Xie, M. Zhang, R. Gao and Y. Xie (2022 b
2022
Later among the works it cites.
F. Al Taha, S. Yan and E. Bitar (2023), A distributionally robust approach to regret optimal control using the Wasserstein distance, in IEEE Conference on Decision and Control
2023
Later among the works it cites.
J. M. Altschuler and E. Boix-Adsera (2023), Polynomial-time algorithms for multimarginal optimal transport problems with structure, Mathematical Programming
2023
Later among the works it cites.
W. Azizian, F. Iutzeler and J. Malick (2023 a
2023
Later among the works it cites.
W. Azizian, F. Iutzeler and J. Malick (2023 b
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Bennouna and B. P. Van Parys (2023), Holistic robust data-driven decisions, arXiv:2207.09560
2023
Later among the works it cites.
D. Bertsimas, S. Shtern and B. Sturt (2023), A data-driven approach to multistage stochastic linear optimization, Management Science
2023
Later among the works it cites.
J. Blanchet and A. Shapiro (2023), Statistical limit theorems in distributionally robust optimization, in Winter Simulation Conference
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Bungert, N. García Trillos and R. Murray (2023), The geometry of adversarial training in binary classification, Information and Inference: A Journal of the IMA
2023
Later among the works it cites.
J. Cai, J. Y.-M. Li and T. Mao (2023), Distributionally robust optimization under distorted expectations, Operations Research (Forthcoming)
2023
Later among the works it cites.
L. Chen, C. Fu, F. Si, M. Sim and P. Xiong (2023), Robust optimization with moment-dispersion ambiguity, SSRN preprint 4525224
2023
Later among the works it cites.
I. Diakonikolas and D. M. Kane (2023), Algorithmic High-Dimensional Robust Statistics
2023
Later among the works it cites.
M. Z. Diao, K. Balasubramanian, S. Chewi and A. Salim (2023), Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein space, in International Conference on Machine Learning
2023
Later among the works it cites.
J. Duchi, T. Hashimoto and H. Namkoong (2023), Distributionally robust losses for latent covariate mixtures, Operations Research
2023
Later among the works it cites.
N. Fournier (2023), Convergence of the empirical measure in expected Wasserstein distance: Non-asymptotic explicit bounds in ℝ d {\mathbb{R}}^{d} , ESAIM: Probability and Statistics
2023
Later among the works it cites.
A. Ganguly and T. Sutter (2023), Optimal learning via moderate deviations theory, arXiv:2305.14496
2023
Later among the works it cites.
R. Gao (2023), Finite-sample guarantees for Wasserstein distributionally robust optimization: Breaking the curse of dimensionality, Operations Research
2023
Later among the works it cites.
R. Gao and A. J. Kleywegt (2023), Distributionally robust stochastic optimization with Wasserstein distance, Mathematics of Operations Research
2023
Later among the works it cites.
N. García Trillos and M. Jacobs (2023), An analytical and geometric perspective on adversarial robustness, Notices of the American Mathematical Society
2023
Later among the works it cites.
N. García Trillos, M. Jacobs and J. Kim (2023), The multimarginal optimal transport formulation of adversarial multiclass classification, Journal of Machine Learning Research
2023
Later among the works it cites.
M. Goerigk and J. Kurtz (2023), Data-driven robust optimization using deep neural networks, Computers & Operations Research
2023
Later among the works it cites.
J. Hajar, T. Kargin and B. Hassibi (2023), Wasserstein distributionally robust regret-optimal control under partial observability, in Allerton Conference on Communication, Control, and Computing
2023
Later among the works it cites.
N. Ho-Nguyen and S. J. Wright (2023), Adversarial classification via distributional robustness with Wasserstein ambiguity, Mathematical Programming
2023
Later among the works it cites.
S. Hou, P. Kassraie, A. Kratsios, A. Krause and J. Rothfuss (2023), Instance-dependent generalization bounds via optimal transport, Journal of Machine Learning Research
2023
Later among the works it cites.
G. Iyengar, H. Lam and T. Wang (2023), Hedging against complexity: Distributionally robust optimization with parametric approximation, pp. 9976–10011
2023
Later among the works it cites.
Z. Liu and P.-L. Loh (2023), Robust W-GAN-based estimation under Wasserstein contamination, Information and Inference: A Journal of the IMA
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
V. A. Nguyen, S. Shafieezadeh-Abadeh, D. Kuhn and P. Mohajerin Esfahani (2023), Bridging bayesian and minimax mean square error estimation via Wasserstein distributionally robust optimization, Mathematics of Operations Research
2023
Later among the works it cites.
S. Peng (2023), G-Gaussian processes under sublinear expectations and q-Brownian motion in quantum mechanics, Numerical Algebra, Control and Optimization
2023
Later among the works it cites.
Y. Ruan, X. Li, K. Murthy and K. Natarajan (2023), A nonparametric approach with marginals for modeling consumer choice, in Conference on Economics and Computation
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Shapiro, E. Zhou and Y. Lin (2023), Bayesian distributionally robust optimization, SIAM Journal on Optimization
2023
Later among the works it cites.
K. S. Shehadeh (2023), Distributionally robust optimization approaches for a stochastic mobile facility fleet sizing, routing, and scheduling problem, Transportation Science
2023
Later among the works it cites.
H. Shen and R. Jiang (2023), Chance-constrained set covering with Wasserstein ambiguity, Mathematical Programming
2023
Later among the works it cites.
L. Sun, W. Xie and T. Witten (2023), Distributionally robust fair transit resource allocation during a pandemic, Transportation Science
2023
Later among the works it cites.
B. Taşkesen, S. Shafieezadeh-Abadeh and D. Kuhn (2023 a
2023
Later among the works it cites.
B. Taşkesen, S. Shafieezadeh-Abadeh, D. Kuhn and K. Natarajan (2023 b
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Zhen, D. Kuhn and W. Wiesemann (2023), A unified theory of robust and distributionally robust optimization via the primal-worst-equals-dual-best principle, Operations Research (Forthcoming)
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Anunrojwong, S. R. Balseiro and O. Besbes (2024), On the robustness of second-price auctions in prior-independent mechanism design, Operations Research (Forthcoming)
2024
Closest in time.
X. Bai, G. He, Y. Jiang and J. Obloj (2024), Wasserstein distributional robustness of neural networks, in Advances in Neural Information Processing Systems
2024
Closest in time.
J. Blanchet, H. Lam, Y. Liu and R. Wang (2024 a
2024
Closest in time.
J. Blanchet, J. Li, S. Lin and X. Zhang (2024 b
2024
Closest in time.
L. Bungert, T. Laux and K. Stinson (2024), A mean curvature flow arising in adversarial training, Journal de Mathématiques Pures et Appliquées
2024
Closest in time.
2024
Closest in time.
L. Chen and M. Sim (2024), Robust CARA optimization, Operations Research (Forthcoming)
2024
Closest in time.
Z. Chen, Z. Hu and R. Wang (2024 a
2024
Closest in time.
Z. Chen, D. Kuhn and W. Wiesemann (2024 b
2024
Closest in time.
N. Frank and J. Niles-Weed (2024 a
2024
Closest in time.
N. S. Frank and J. Niles-Weed (2024 b
2024
Closest in time.
R. Gao, R. Arora and Y. Huang (2024 a
2024
Closest in time.
R. Gao, X. Chen and A. J. Kleywegt (2024 b
2024
Closest in time.
A. Hakobyan and I. Yang (2024), Wasserstein distributionally robust control of partially observable linear stochastic systems, IEEE Transactions on Automatic Control
2024
Closest in time.
2024
Closest in time.
N. Jiang and W. Xie (2024), Distributionally favorable optimization: A framework for data-driven decision-making with endogenous outliers, SIAM Journal on Optimization
2024
Closest in time.
2024
Closest in time.
Y. Jiang, S. Chewi and A.-A. Pooladian (2024), Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space, in Conference on Learning Theory
2024
Closest in time.
T. Kargin, J. Hajar, V. Malik and B. Hassibi (2024 a
2024
Closest in time.
T. Kargin, J. Hajar, V. Malik and B. Hassibi (2024 b
2024
Closest in time.
T. Kargin, J. Hajar, V. Malik and B. Hassibi (2024 c
2024
Closest in time.
T. Kargin, J. Hajar, V. Malik and B. Hassibi (2024 d
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
F. Liu, Z. Chen, R. Wang and S. Wang (2024 a
2024
Closest in time.
J. Liu, Z. Su and H. Xu (2024 b
2024
Closest in time.
D. Z. Long, J. Qi and A. Zhang (2024), Supermodularity in two-stage distributionally robust optimization, Management Science
2024
Closest in time.
R. D. McAllister and P. M. Esfahani (2024), Distributionally robust model predictive control: Closed-loop guarantees and scalable algorithms, IEEE Transactions on Automatic Control (in press)
2024
Closest in time.
V. A. Nguyen, F. Zhang, S. Wang, J. Blanchet, E. Delage and Y. Ye (2024), Robustifying conditional portfolio decisions via optimal transport, Operations Research (Forthcoming)
2024
Closest in time.
S. Nietert, Z. Goldfeld and S. Shafiee (2024 a
2024
Closest in time.
S. Nietert, Z. Goldfeld and S. Shafiee (2024 b
2024
Closest in time.
S. Pesenti, Q. Wang and R. Wang (2024), Optimizing distortion riskmetrics with distributional uncertainty, Mathematical Programming (Forthcoming)
2024
Closest in time.
Y. Polyanskiy and Y. Wu (2024), Information Theory: From Coding to Learning
2024
Closest in time.
K. Postek and S. Shtern (2024), First-order algorithms for robust optimization problems via convex-concave saddle-point Lagrangian reformulation, INFORMS Journal on Computing (Forthcoming)
2024
Closest in time.
M. S. Pydi and V. Jog (2024), The many faces of adversarial risk: An expanded study, IEEE Transactions on Information Theory
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
S. Shafiee and D. Kuhn (2024), Minimax theorems and Nash equilibria in distributionally robust optimization problems, Working Paper
2024
Closest in time.
M. R. Sheriff and P. Mohajerin Esfahani (2024), Nonlinear distributionally robust optimization, Mathematical Programming (in press)
2024
Closest in time.
B. Taşkesen, D. Iancu, Ç. Koçyiğit and D. Kuhn (2024), Distributionally robust linear quadratic control, in Advances in Neural Information Processing Systems
2024
Closest in time.
A. Terpin, N. Lanzetti and F. Dörfler (2024), Dynamic programming in probability spaces via optimal transport, SIAM Journal on Control and Optimization
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
B. P. Van Parys (2024), Efficient data-driven optimization with noisy data, Operations Research Letters
2024
Closest in time.
B. P. Van Parys and N. Golrezaei (2024), Optimal learning for structured bandits, Management Science
2024
Closest in time.
F. Vincent, W. Azizian, J. Malick and F. Iutzeler (2024), skwdro
2024
Closest in time.
I. Wang, C. Becker, B. Van Parys and B. Stellato (2024 a
2024
Closest in time.
J. Wang, R. Gao and Y. Xie (2024 b
2024
Closest in time.
S. Wang (2024), The power of simple menus in robust selling mechanisms, Management Science (Forthcoming)
2024
Closest in time.
S. Wang, S. Liu and J. Zhang (2024 c
2024
Closest in time.
Y. Wang, V. A. Nguyen and G. A. Hanasusanto (2024 d
2024
Closest in time.
Y. Wang, M. N. Prasad, G. A. Hanasusanto and J. J. Hasenbein (2024 e
2024
Closest in time.
C. Xu, J. Lee, X. Cheng and Y. Xie (2024), Flow-based distributionally robust optimization, IEEE Journal on Selected Areas in Information Theory
2024
Closest in time.
L. Zhang, J. Yang and R. Gao (2024 a
2024
Closest in time.
L. Zhang, J. Yang and R. Gao (2024 b
2024
Closest in time.
H. I. Bayrak, Ç. Koçyiğit, D. Kuhn and M. C. Pınar (2025), Distributionally robust optimal allocation with costly verification, Operations Research (in press)
2025
Closest in time.
J. Milz and M. Ulbrich (2020), An approximation scheme for distributionally robust nonlinear optimization, SIAM Journal on Optimization
2025
Closest in time.
W. J. van Eekelen, G. A. Hanasusanto, J. J. Hasenbein and J. S. van Leeuwaarden (2025), Second-order bounds for the M/M/s queue with random arrival rate, Queueing Systems
2025
Closest in time.
K. Roth, A. Lucchi, S. Nowozin and T. Hofmann (2017), Stabilizing training of generative adversarial networks through regularization, in Advances in Neural Information Processing Systems
2028
Closest in time.
N. Gravin and P. Lu (2018), Separation in correlation-robust monopolist problem with budget, in SIAM Symposium on Discrete Algorithms
2080
Closest in time.
S. Bose and A. Daripa (2009), A dynamic mechanism and surplus extraction under ambiguity, Journal of Economic theory
2084
Closest in time.
N. Rujeerapaiboon, D. Kuhn and W. Wiesemann (2016), Robust growth-optimal portfolios, Management Science
2090
Closest in time.
A. Bennouna, R. Lucas and B. P. Van Parys (2023), Certified robust neural networks: Generalization and corruption resistance, in International Conference on Machine Learning
2092
Closest in time.