Research
Advancing data systems and analytics that enable scalable insights, intelligent applications, and scientific discovery.
Traditional CPUs are general, but this comes at a cost (called “Turing tax”). They are more expensive and less efficient than specialized hardware. This raises the question: should we build CPU-free nodes? CPU-free nodes only…
Online Learning Rate Adaptation with Hypergradient Descent: We introduce a general method for improving the convergence rate of gradient-based optimizers that is easy to implement and works well in practice. We demonstrate the effectiveness of…
A framework to host and train publicly available machine learning models while crowdsourcing a dataset. Ideally, using a model for prediction is free. An incentive mechanism validates added data.
We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model.
For millions of patients across the US, hospitals use commercial risk scores to target those needing extra help with complex health needs. We examine a widely used commercial algorithm for racial bias. Thanks to a…
