We design new algorithms that leverage tools from survival analysis to provably perform well in these settings. We demonstrate the value of our approach on a large-scale clinical trial database on metastatic breast cancer.…Read More
This project adapts online learning techniques to unstructured data, focusing on neural networks. We posit a non-parametric multi-armed bandit algorithm with context that uses a neural network for its underlying function approximation. We theoretically analyze and prove guarantees on the regret under this algorithm…Read More
High resource constraints and the covert nature of sex trafficking provide significant barriers to developing data-driven innovations that inform law enforcement investigations and trials. We leverage massive deep web data (collected globally from leading adult services websites) in tandem with a novel machine learning framework to unmask sex-trafficking recruitment-to-sales pathways.…Read More
In this paper, we propose combining data from both surrogate and true outcomes to improve decision-making within a late-phase clinical trial.…Read More
The goal of our paper is to study whether machine learning can be used to infer tips that can help workers learn to make better decisions. …Read More
This proposal requests research funding for the development of two papers that propose novel methods for leveraging large amounts of imprecise information along with small amounts of specific information to improve the efficacy of medical treatment. …Read More
