Calvin Tsay

According to our database, Calvin Tsay authored at least 27 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Constrained continuous-action reinforcement learning for supply chain inventory management.

Radu Burtea, Calvin Tsay

Comput. Chem. Eng., February, 2024

Transition Constrained Bayesian Optimization via Markov Decision Processes.

Jose Pablo Folch, Calvin Tsay, Robert M. Lee, Behrang Shafei, Weronika Ormaniec, Andreas Krause, Mark van der Wilk, Ruth Misener, Mojmír Mutný

CoRR, 2024

Bayesian optimization as a flexible and efficient design framework for sustainable process systems.

Joel A. Paulson, Calvin Tsay

CoRR, 2024

2023

Combining multi-fidelity modelling and asynchronous batch Bayesian Optimization.

Jose Pablo Folch, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener

Comput. Chem. Eng., April, 2023

Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design.

Tom McDonald, Calvin Tsay, Artur M. Schweidtmann, Neil Yorke-Smith

CoRR, 2023

Practical Path-based Bayesian Optimization.

Jose Pablo Folch, James Odgers, Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener

CoRR, 2023

When Deep Learning Meets Polyhedral Theory: A Survey.

Joey Huchette, Gonzalo Muñoz, Thiago Serra, Calvin Tsay

CoRR, 2023

Distributional constrained reinforcement learning for supply chain optimization.

Jaime Sabal Bermúdez, Antonio del Rio-Chanona, Calvin Tsay

CoRR, 2023

Optimizing over trained GNNs via symmetry breaking.

Shiqiang Zhang, Juan S. Campos, Christian Feldmann, David Walz, Frederik Sandfort, Miriam Mathea, Calvin Tsay, Ruth Misener

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Model-Based Feature Selection for Neural Networks: A Mixed-Integer Programming Approach.

Shudian Zhao, Calvin Tsay, Jan Kronqvist

Proceedings of the Learning and Intelligent Optimization - 17th International Conference, 2023

2022

OMLT: Optimization & Machine Learning Toolkit.

Francesco Ceccon, Jordan Jalving, Joshua Haddad, Alexander Thebelt, Calvin Tsay, Carl D. Laird, Ruth Misener

J. Mach. Learn. Res., 2022

P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints.

Jan Kronqvist, Ruth Misener, Calvin Tsay

CoRR, 2022

Maximizing information from chemical engineering data sets: Applications to machine learning.

Alexander Thebelt, Johannes Wiebe, Jan Kronqvist, Calvin Tsay, Ruth Misener

CoRR, 2022

Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces.

Alexander Thebelt, Calvin Tsay, Robert M. Lee, Nathan Sudermann-Merx, David Walz, Behrang Shafei, Ruth Misener

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

SnAKe: Bayesian Optimization with Pathwise Exploration.

Jose Pablo Folch, Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles.

Alexander Thebelt, Calvin Tsay, Robert M. Lee, Nathan Sudermann-Merx, David Walz, Tom Tranter, Ruth Misener

CoRR, 2021

Sobolev trained neural network surrogate models for optimization.

Calvin Tsay

Comput. Chem. Eng., 2021

Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks.

Calvin Tsay, Jan Kronqvist, Alexander Thebelt, Ruth Misener

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Between Steps: Intermediate Relaxations Between Big-M and Convex Hull Formulations.

Jan Kronqvist, Ruth Misener, Calvin Tsay

Proceedings of the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 2021

2020

Identification and online updating of dynamic models for demand response of an industrial air separation unit.

Calvin Tsay, Yanan Cao, Yajun Wang, Jesus Flores-Cerrillo, Michael Baldea

CoRR, 2020

2019

Learning latent variable dynamic models for integrated production scheduling and control.

Calvin Tsay, Michael Baldea

CoRR, 2019

Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models.

Calvin Tsay, Ankur Kumar, Jesus Flores-Cerrillo, Michael Baldea

Comput. Chem. Eng., 2019

Automating Visual Inspection of Lyophilized Drug Products With Multi-Input Deep Neural Networks.

Calvin Tsay, Zheng Li

Proceedings of the 15th IEEE International Conference on Automation Science and Engineering, 2019

2018

A survey of optimal process design capabilities and practices in the chemical and petrochemical industries.

Calvin Tsay, Richard C. Pattison, Michael R. Piana, Michael Baldea

Comput. Chem. Eng., 2018

A simulation-based optimization framework for integrating scheduling and model predictive control, and its application to air separation units.

Lisia S. Dias, Richard C. Pattison, Calvin Tsay, Michael Baldea, Marianthi G. Ierapetritou

Comput. Chem. Eng., 2018

2017

A superstructure-based design of experiments framework for simultaneous domain-restricted model identification and parameter estimation.

Calvin Tsay, Richard C. Pattison, Michael Baldea, Ben G. Weinstein, Steven J. Hodson, Robert D. Johnson

Comput. Chem. Eng., 2017

Pseudo-transient models for multiscale, multiresolution simulation and optimization of intensified reaction/separation/recycle processes: Framework and a dimethyl ether production case study.

Richard C. Pattison, Calvin Tsay, Michael Baldea

Comput. Chem. Eng., 2017