Mateo Dulce Rubio

We propose Conformal Mixed-Integer Constraint Learning, a novel framework that provides probabilistic feasibility guarantees for data-driven constraints in optimization problems.

We propose a sequential test for auditing differential privacy that detects violations with sample sizes that are orders of magnitude smaller than existing methods.