Search Results for author:
Found 7 papers, 2 papers with code
Two-Stage Predict+Optimize for Mixed Integer Linear Programs with Unknown Parameters in Constraints
We also give a training algorithm usable for all mixed integer linear programs, vastly generalizing the applicability of the framework.
Branch & Learn with Post-hoc Correction for Predict+Optimize with Unknown Parameters in Constraints
Combining machine learning and constrained optimization, Predict+Optimize tackles optimization problems containing parameters that are unknown at the time of solving.
Predict+Optimize for Packing and Covering LPs with Unknown Parameters in Constraints
First, we propose a novel and practically relevant framework for the Predict+Optimize setting, but with unknown parameters in both the objective and the constraints.
iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and Recognition
In this paper, we propose iExam, an intelligent online exam monitoring and analysis system that can not only use face detection to assist invigilators in real-time student identification, but also be able to detect common abnormal behaviors (including face disappearing, rotating faces, and replacing with a different person during the exams) via a face recognition-based post-exam video analysis.
Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize
This paper proposes Branch & Learn, a framework for Predict+Optimize to tackle optimization problems containing parameters that are unknown at the time of solving.
Augmenting Stream Constraint Programming with Eventuality Conditions
Stream constraint programming is a recent addition to the family of constraint programming frameworks, where variable domains are sets of infinite streams over finite alphabets.
Tractability and Decompositions of Global Cost Functions
A global cost function is called tractable projection-safe when applying an EPT to it is tractable and does not break the tractability property.
