Optimization Research
Research at the intersection of big data, optimization, and explainability
Typal Academy's research efforts focus on open-source development of optimization-based tools. Our specialty is in creating optimization models and algorithms that are tunable, enabling high performance on a classes of applications where a distribution of training data is available.
We are happy to share our work published via @PNASNews. We give a simple formula for estimating proximal operators when access is only given to (possibly noisy) objective function samples; these estimates can be embedded in #optimization algorithms.https://t.co/luNNxGFUjY
— Typal Academy (@TypalAcademy) August 4, 2023
We are happy to share our work on #explainableAI was recently published in #ScientificReports. We show how to use #optimization with deep learning to make explainable models and explainable inferences, using certificates of trustworthiness.
— Typal Academy (@TypalAcademy) July 5, 2023
Check it out:https://t.co/YMfPDZZxw4
Learning to Optimize
Faster Predict-and-Optimize with Davis-Yin Splitting
Safeguarded Learned Convex Optimization
Learn to Predict EQ via Fixed Point Networks
Feasibility-based Fixed Point Networks
Zero-Order Optimization
Global Solutions to Nonconvex Problems by Evolution of HJ PDEs