Search Results
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Stemming from the potential outcomes model, we propose a novel multi- task learning framework in which factual and counterfactual outcomes are mod- eled as the outputs of a function in a vector-valued reproducing kernel Hilbert space (vvRKHS).
Reinforcement Learning with Dynamic Convex Risk Measures
We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement learning (RL).
Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic Programming
To avoid these breaches and formally guarantee privacy to optimization data owners, we develop a new privacy-preserving perturbation strategy for convex optimization programs by combining stochastic (chance-constrained) programming and differential privacy.
Optimization and Control
Minimizing Oracle-Structured Composite Functions
For the structured function, systems like CVXPY accept a high level domain specific language description of the problem, and automatically translate it to a standard form for efficient solution.
Optimization and Control
ALSO-X and ALSO-X+: Better Convex Approximations for Chance Constrained Programs
In a chance constrained program (CCP), the decision-makers aim to seek the best decision whose probability of violating the uncertainty constraints is within the prespecified risk level.
Optimization and Control
COVID-19 in low-tolerance border quarantine systems: impact of the Delta variant of SARS-CoV-2
We examine disease transmission and vaccine efficacy parameters over a wide range, covering plausible values for the Delta variant currently circulating globally.
