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Guided Evolution with Binary Predictors for ML Program Search
John Co-Reyes · Yingjie Miao · George Tucker · Aleksandra Faust · Esteban Real
Event URL: https://openreview.net/forum?id=hSSZKdk3Sg »

Primitive-based evolutionary AutoML discovers novel state-of-the-art ML components by searching over programs built from low-level building blocks. While very expressive, these spaces have sparsely distributed good performing candidates. This poses great challenges in efficient search. Performance predictors have proven successful in speeding up search in smaller and denser Neural Architecture Search (NAS) spaces, but they have not yet been tried on these larger primitive-based search spaces. Through a unified graph representation to encode a wide variety of ML components, we train a binary classifier online to predict which of two given candidates is better. We then present an adaptive mutation method that leverages the learned binary predictor and show how it improves local search. We empirically demonstrate our method speeds up end-to-end evolution across a set of diverse problems including a 3.7x speedup on the symbolic search for ML optimizers and a 4x speedup for RL loss functions.

Author Information

John Co-Reyes (UC Berkeley)
Yingjie Miao (Google)
George Tucker (Google Brain)
Aleksandra Faust (Google Brain)

Aleksandra Faust is a Staff Research Scientist at Google Brain Robotics, leading Task and Motion planning research group. Previously, Aleksandra led machine learning efforts for self-driving car planning and controls in Waymo, and was a researcher at Sandia National Laboratories. She earned a Ph.D. in Computer Science at the University of New Mexico, a Master's in Computer Science from the University of Illinois at Urbana-Champaign, and a Bachelors in Math with a minor in Computer Science from the University of Belgrade. Her research interests include machine learning for safe, scalable, and socially-aware motion planning, decision-making, and robot behavior. Aleksandra won the Tom L. Popejoy Award for the best doctoral dissertation at the University of New Mexico in STEM in the period of 2011-2014, and was named Distinguished Alumna by the University of New Mexico School of Engineering. Her work has been featured in the New York Times, PC Magazine, ZdNet, and ​was awarded Best Paper in Service Robotics at ICRA 2018.

Esteban Real (Google Research)

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