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pro vyhledávání: '"Lee, Jimmy H. M."'
Consider the setting of constrained optimization, with some parameters unknown at solving time and requiring prediction from relevant features. Predict+Optimize is a recent framework for end-to-end training supervised learning models for such predict
Externí odkaz:
http://arxiv.org/abs/2311.08022
Combining machine learning and constrained optimization, Predict+Optimize tackles optimization problems containing parameters that are unknown at the time of solving. Prior works focus on cases with unknowns only in the objectives. A new framework wa
Externí odkaz:
http://arxiv.org/abs/2303.06698
Predict+Optimize is a recently proposed framework which combines machine learning and constrained optimization, tackling optimization problems that contain parameters that are unknown at solving time. The goal is to predict the unknown parameters and
Externí odkaz:
http://arxiv.org/abs/2209.03668
Online exams via video conference software like Zoom have been adopted in many schools due to COVID-19. While it is convenient, it is challenging for teachers to supervise online exams from simultaneously displayed student Zoom windows. In this paper
Externí odkaz:
http://arxiv.org/abs/2206.13356
This paper proposes Branch & Learn, a framework for Predict+Optimize to tackle optimization problems containing parameters that are unknown at the time of solving. Given an optimization problem solvable by a recursive algorithm satisfying simple cond
Externí odkaz:
http://arxiv.org/abs/2205.01672
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. Previous works showed promising results for its applicability to real-wo
Externí odkaz:
http://arxiv.org/abs/1806.04325
Autor:
Allouche, David, Bessiere, Christian, Boizumault, Patrice, de Givry, Simon, Gutierrez, Patricia, Lee, Jimmy H. M., Leung, Kam Lun, Loudni, Samir, Métivier, Jean-Philippe, Schiex, Thomas, Wu, Yi
Enforcing local consistencies in cost function networks is performed by applying so-called Equivalent Preserving Transformations (EPTs) to the cost functions. As EPTs transform the cost functions, they may break the property that was making local con
Externí odkaz:
http://arxiv.org/abs/1502.02414
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Akademický článek
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Autor:
Lee, Jimmy H. M., Zhong, Allen Z.
Dominance breaking is an effective technique to reduce the time for solving constraint optimization problems. Lee and Zhong propose an automatic dominance breaking framework for a class of constraint optimization problems based on specific forms of o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5f106c3da2d1e59b94c61ae89f978983