21 citations · 21 across the 5 of their papers we have counts for
5 papers
Robust Chance-Constrained Optimization using a Continuous Parameter Space Wasserstein-2 Ambiguity Set of Gaussian Mixtures
Shibshankar Dey, Sanjay Mehrotra
We study distributionally robust linear chance-constrained problems in which uncertainty is modeled by a Gaussian mixture model (GMM). Finite-support distributionally robust (FDR)…
Do Deep Networks Forget Initialization? A Forgetting-Time View of Practical Inductive Bias
Mohua Das, Pierfrancesco Beneventano, Shibshankar Dey +2
Randomly initialized neural networks induce a prior over functions, but the predictor used in practice is produced only after training. We ask how much of this initial bias survive…
On Solving Chance-Constrained Models with Gaussian Mixture Distribution
Shibshankar Dey, Sanjay Mehrotra, Anirudh Subramanyam
We study linear chance-constrained problems where the coefficients follow a Gaussian mixture distribution. We provide mixed-binary quadratic programs that give inner and outer appr…
Optimization Modeling for Pandemic Vaccine Supply Chain Management: A Review and Future Research Opportunities
Shibshankar Dey, Ali Kaan Kurbanzade, Esma S. Gel +2
During various stages of the COVID-19 pandemic, countries implemented diverse vaccine management approaches, influenced by variations in infrastructure and socio-economic condition…
A Cutting-plane and Benders' Decomposition Algorithm for Two-Stage Distributionally Robust Convex programs
Fengqiao Luo, Shibshankar Dey, Sanjay Mehrotra
We present a finitely convergent cutting-plane algorithm for solving a general mixed-integer convex program given an oracle for solving a general convex program. This method is ext…