Bryon Aragam, Arash A Amini, and Qing Zhou. Learning directed acyclic graphs with penalized neighbourhood regression. arXiv preprint arXiv:1511.08963, 2015.
Adarsh Bank and Jean Honorio. Provable efficient skeleton learning of encodable discrete bayes nets in poly-time and sample complexity. In 2020 IEEE International Symposium on Information Theory (ISIT), pages 2486-2491. IEEE, 2020.
Dimitris Bertsimas, Kosuke Imai, and Michael Lingzhi Li. Distributionally robust causal inference with observational data. arXiv preprint arXiv:2210.08326, 2022.
Jose Blanchet and Karthyek Murthy. Quantifying distributional model risk via optimal transport. Mathematics of Operations Research, 44(2):565-600, 2019.
Richard H Byrd, Peihuang Lu, Jorge Nocedal, and Ciyou Zhu. A limited memory algorithm for bound constrained optimization. SIAM Journal on scientific computing, 16(5):1190-1208, 1995.
Moses Charikar and Anthony Wirth. Maximizing quadratic programs: Extending grothendieck's inequality. In 45th Annual IEEE Symposium on Foundations of Computer Science, pages 54-60. IEEE, 2004.
Ruidi Chen and Ioannis C Paschalidis. A robust learning approach for regression models based on distributionally robust optimization. Journal of Machine Learning Research, 19(13), 2018.
Wenyu Chen, Mathias Drton, and Y Samuel Wang. On causal discovery with an equal-variance assumption. Biometrika, 106(4):973-980, 2019.
David Maxwell Chickering. Optimal structure identification with greedy search. Journal of machine learning research, 3(Nov):507-554, 2002.
Max Chickering, David Heckerman, and Chris Meek. Large-sample learning of bayesian networks is np-hard. Journal of Machine Learning Research, 5:1287-1330, 2004.
Pedro Cisneros-Velarde, Alexander Petersen, and Sang-Yun Oh. Distributionally robust formulation and model selection for the graphical lasso. In International Conference on Artificial Intelligence and Statistics, pages 756-765. PMLR, 2020.
Diego Colombo, Marloes H Maathuis, et al. Order-independent constraint-based causal structure learning. J. Mach. Learn. Res., 15(1):3741-3782, 2014.
Anthony C Constantinou, Yang Liu, Kiattikun Chobtham, Zhigao Guo, and Neville K Kitson. Large-scale empirical validation of bayesian network structure learning algorithms with noisy data. International Journal of Approximate Reasoning, 131:151-188, 2021.
Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, and Simon Kornblith. Generalised lipschitz regularisation equals distributional robustness. In International Conference on Machine Learning, pages 2178-2188. PMLR, 2021.
Hadi Daneshmand, Manuel Gomez-Rodriguez, Le Song, and Bernhard Schoelkopf. Estimating diffusion network structures: Recovery conditions, sample complexity & soft-thresholding algorithm. In International conference on machine learning, pages 793-801. PMLR, 2014.
Erick Delage and Yinyu Ye. Distributionally robust optimization under moment uncertainty with application to data-driven problems. Operations research, 58(3):595-612, 2010.
Rod G Downey and Michael R Fellows. Fixed-parameter tractability and completeness i: Basic results. SIAM Journal on computing, 24(4):873-921, 1995.
Mathias Drton and Marloes H Maathuis. Structure learning in graphical modeling. Annual Review of Statistics and Its Application, 4:365-393, 2017.
John Duchi and Hongseok Namkoong. Variance-based regularization with convex objectives. The Journal of Machine Learning Research, 20(1):2450-2504, 2019.