Fetching the paper…
Reading the bibliography…
Distributionally robust optimization (DRO) problems are increasingly seen as a viable method to train machine learning models for improved model generalization.
OpenML-Python: an extensible Python API for OpenML
Feurer, M.; van Rijn, J. N.; Kadra, A.; Gijsbers, P.; Mallik, N.; Ravi, S.; Mueller, A.; Vanschoren, J.; and Hutter, F. 2019 · 1911
Earlier work this paper cites.
Wilks, S. S. 1962 · 1962
Earlier work this paper cites.
Optimization by Vector Space Methods
Luenberger, D. G. 1969 · 1969
Earlier work this paper cites.
Cross-validatory choice and assessment of statistical predictions
Stone, M. 1974 · 1974
Earlier work this paper cites.
Second-Order Derivatives of Extremal-Value Functions and Optimality Conditions for Semi-Infinite Programs
Shapiro, A. 1985 · 1985
Earlier work this paper cites.
Spline Models for Observational Data. Society for Industrial and Applied Mathematics
Wahba, G. 1990 · 1990
Earlier work this paper cites.
Statistical Learning Theory
Vapnik, V. N. 1998 · 1998
Earlier work this paper cites.
Empirical Likelihood
Owen, A. B. 2001 · 2001
Earlier work this paper cites.
Tree Induction vs. Logistic Regression: A Learning-Curve Analysis
Perlich, C.; Provost, F.; and Simonoff, J. S. 2003 · 2003
Earlier work this paper cites.
Probability Essentials
Jacod, J.; and Protter, P. 2004 · 2004
Earlier work this paper cites.
RCV1: A new benchmark collection for text categorization research
Lewis, D.; Yang, Y.; Rose, T.; and Li, F. 2004 · 2004
Cited alongside, same era.
Worst-case distribution analysis of stochastic programs
Shapiro, A. 2006 · 2006
Cited alongside, same era.
Introduction to the Theory of Computation
Sipser, M. 2006 · 2006
Cited alongside, same era.
Lectures on Stochastic Programming Modeling and Theory
Shapiro, A.; Dentcheva, D.; and Ruszczynski, A. 2009 · 2009
Cited alongside, same era.
Robust solutions of optimization problems affected by uncertain probabilities
Ben-Tal, A.; Den Hertog, D.; De Waegenaere, A.; Melenberg, B.; and Rennen, G. 2013 · 2013
Cited alongside, same era.
UCI Machine Learning Repository
Lichman, M. 2013 · 2013
Cited alongside, same era.
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences
Namkoong, H.; and Duchi, J. C. 2016 · 2016
Later among the works it cites.
Wasserstein Distributional Robustness and Regularization in Statistical Learning
Gao, R.; Chen, X.; and Kleywegt, A. J. 2017 · 2017
Later among the works it cites.
The Adaptive Sampling Gradient Method: Optimizing Smooth Functions with an Inexact Oracle Under review
Hashemi, F. S.; Pasupathy, R.; and Taaffe, M. R. 2017 · 2017
Later among the works it cites.
Decomposition Algorithm for Distributionally Robust Optimization using Wasserstein Metric
Luo, F.; and Mehrotra, S. 2017 · 2017
Later among the works it cites.
Variance-based Regularization with Convex Objectives
Namkoong, H.; and Duchi, J. C. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
State of the art prediction of HIV-1 protease cleavage sites
Rognvaldsson, T.; You, L.; and Garwicz, D. 2014 · 2014
Cited alongside, same era.
Robust Wasserstein Profile Inference and Applications to Machine Learning
Blanchet, J.; Kang, Y.; and Murthy, K. 2016 · 2016
Cited alongside, same era.
Optimization Methods for Large-Scale Machine Learning
Bottou, L.; Curtis, F. E.; and Nocedal, J. 2016 · 2016
Cited alongside, same era.
Distributionally Robust Stochastic Optimization with Wasserstein Distance
Gao, R.; and Kleywegt, A. J. 2016 · 2016
Cited alongside, same era.
A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization
Chen, R.; and Paschalidis, I. 2018 · 2018
Closest in time.
Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
Esfahani, P.; and Kuhn, D. 2018 · 2018
Closest in time.
Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees
Ghosh, S.; and Lam, H. 2018 · 2018
Closest in time.
On Sampling Rates in Simulation-Based Recursions
Pasupathy, R.; Glynn, P.; Ghosh, S.; and Hashemi, F. 2018 · 2018
Closest in time.
Certifying Some Distributional Robustness with Principled Adversarial Training
Sinha, A.; Namkoong, H.; and Duchi, J. 2017 · 2018
Closest in time.