Machine Learning with Operational Costs
This work proposes a way to align statistical modeling with decision making. We provide a method that propagates the uncertainty in predictive modeling to the uncertainty in operational cost, where operational cost is the amount spent by the practitioner in solving the problem. The method allows us...
Main Authors: | Tulabandhula, Theja (Contributor), Rudin, Cynthia (Contributor) |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Operations Research Center (Contributor), Sloan School of Management (Contributor) |
Format: | Article |
Language: | English |
Published: |
Association for Computing Machinery (ACM),
2013-10-18T13:26:17Z.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
The Influence of Operational Cost on Estimation
by: Tulabandhula, Theja, et al.
Published: (2014) -
On combining machine learning with decision making
by: Tulabandhula, Theja, et al.
Published: (2016) -
The Machine Learning and Traveling Repairman Problem
by: Tulabandhula, Theja, et al.
Published: (2012) -
Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge
by: Tulabandhula, Theja, et al.
Published: (2016) -
Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing
by: Tulabandhula, Theja, et al.
Published: (2017)