all AI news
Residuals-based distributionally robust optimization with covariate information. (arXiv:2012.01088v2 [math.OC] UPDATED)
May 26, 2022, 1:11 a.m. | Rohit Kannan, Güzin Bayraksan, James R. Luedtke
stat.ML updates on arXiv.org arxiv.org
We consider data-driven approaches that integrate a machine learning
prediction model within distributionally robust optimization (DRO) given
limited joint observations of uncertain parameters and covariates. Our
framework is flexible in the sense that it can accommodate a variety of
regression setups and DRO ambiguity sets. We investigate asymptotic and finite
sample properties of solutions obtained using Wasserstein, sample robust
optimization, and phi-divergence-based ambiguity sets within our DRO
formulations, and explore cross-validation approaches for sizing these
ambiguity sets. Through numerical experiments, …
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Director, Global Procurement Data Analytics
@ Alcon | Fort Worth - Main
Backend Software Engineer, Airbnb for Real Estate
@ Airbnb | United States
Data Scientist
@ Exoticca | Barcelona, Catalonia, Spain - Remote
ESG Data Analytics Summer Associate (Intern)
@ Apex Clean Energy | Charlottesville, VA, United States
Team Lead, Machine Learning
@ Prenuvo | Vancouver, British Columbia, Canada