Welcome! I'm currently a PhD student at the Computer Science department of University of Toronto. My advisor is Gennady Pekhimenko. I am motivated by the possibilities that state-of-the-art machine learning (ML) tools present for addressing challenges within the natural and social sciences. I completed my Master of Science in Machine Learning at Carnegie Mellon University (CMU) and obtained a Bachelor of Arts in Computer Science and History from Columbia University.

Select Publications


NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search
*Renbo Tu, *Nicholas Roberts, Mikhail Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar.
Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks, 2022
Website Code PDF

NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies
Arjun Krishnakumar*, Colin White*, Arber Zela*, Renbo Tu*, Mahmoud Safari, Frank Hutter.
Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks, 2022
PDF Code AutoML for Climate Change: A Call to Action
Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, Colin White.
NeurIPS Workshop Tackling Climate Change with Machine Learning, 2022
PDF Code Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar.
Neural Information Processing Systems (NeurIPS), 2021
PDF Code

A Deeper Look at Zero-Cost Proxies for Lightweight NAS
Colin White, Mikhail Khodak, Renbo Tu, Shital Shah, Sébastien Bubeck, Debadeepta Dey.
International Conference on Learning Representations (ICLR) Blog, 2022
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