Shape-constrained convex regression problem deals with fitting a convex function to the observed data, where additional constraints are imposed, such as component-wise monotonicity and uniform Lipschitz continuity. This talk presents […]
Extensive research has shown that human decision makers in operations and supply chains are often influenced by cognitive biases and social preferences, and as a consequence fail to achieve the […]
We present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual […]
Dr. Ying Chen is a financial statistician and data scientist. She develops statistical modelling and machine learning methods customized for nonstationary, high frequency and large dimensional complex data such as […]
James Zou is an assistant professor of biomedical data science, CS and EE at Stanford University. He is also a Chan-Zuckerberg investigator. James work on making ML more reliable, accountable […]
