Privileged Zero-Shot AutoML

no code implementations • 25 Jun 2021 • Nikhil Singh, Brandon Kates, Jeff Mentch, Anant Kharkar, Madeleine Udell, Iddo Drori

This work improves the quality of automated machine learning (AutoML) systems by using dataset and function descriptions while significantly decreasing computation time from minutes to milliseconds by using a zero-shot approach.

AutoML BIG-bench Machine Learning +1

Real-Time AutoML

no code implementations • 1 Jan 2021 • Iddo Drori, Brandon Kates, Anant Kharkar, Lu Liu, Qiang Ma, Jonah Deykin, Nihar Sidhu, Madeleine Udell

We train a graph neural network in which each node represents a dataset to predict the best machine learning pipeline for a new test dataset.

AutoML BIG-bench Machine Learning +1

Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time

no code implementations • 6 Jun 2020 • Iddo Drori, Anant Kharkar, William R. Sickinger, Brandon Kates, Qiang Ma, Suwen Ge, Eden Dolev, Brenda Dietrich, David P. Williamson, Madeleine Udell

Combinatorial optimization algorithms for graph problems are usually designed afresh for each new problem with careful attention by an expert to the problem structure.

Combinatorial Optimization Traveling Salesman Problem