2 citations · 2 across the 4 of their papers we have counts for
4 papers
Control-Informed Reinforcement Learning for Chemical Processes
Maximilian Bloor, Akhil Ahmed, Niki Kotecha +3
This work proposes a control-informed reinforcement learning (CIRL) framework that integrates proportional-integral-derivative (PID) control components into the architecture of dee…
Bayesian optimization as a flexible and efficient design framework for sustainable process systems
Joel A. Paulson, Calvin Tsay
Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engine…
Model-based feature selection for neural networks: A mixed-integer programming approach
Shudian Zhao, Calvin Tsay, Jan Kronqvist
In this work, we develop a novel input feature selection framework for ReLU-based deep neural networks (DNNs), which builds upon a mixed-integer optimization approach. While the me…
Distributional constrained reinforcement learning for supply chain optimization
Jaime Sabal Bermúdez, Antonio del Rio Chanona, Calvin Tsay
This work studies reinforcement learning (RL) in the context of multi-period supply chains subject to constraints, e.g., on production and inventory. We introduce Distributional Co…