I design algorithms that create advanced AI algorithms.
I am a postdoc at Caltech in the Computing + Mathematical Sciences (CMS) Department, working with Anima Anandkumar. Before that, I was Head of Research at Abacus.AI. I graduated from Carnegie Mellon University with a Ph.D. in computer science, advised by Nina Balcan and supported by the NDSEG Fellowship. I received my undergraduate degree from Amherst College.
For more, see my CV (last updated Apr. 2023).
My work focuses on automating the search for high-performing deep learning models, as well as explaining and de-biasing them, from both a theoretical and empirical lens. Much of my work involves designing innovative methods by drawing insights from large-scale studies (ref1, ref2, ref3) and designing tools for better benchmarking of machine learning techniques (ref4, ref5, ref6).
- • Apr 17, 2023: I started as a postdoc at Caltech!
- • Jan 25, 2023: I gave a talk at UC Berkeley. My slides are here.
- • Jan 22, 2023: New survey on neural architecture search. Email me if you have comments!
- • Dec 1, 2022: I am a Program Chair for AutoML 2023 in Potsdam/Berlin, Germany. Submit your paper by March 23, 2023!
- • Aug 17, 2022: I gave a talk at Microsoft Research. See the slides here.
- • Jul 25, 2022: I gave a tutorial on neural architecture search with Debadeepta Dey, at AutoML 2022. View the talk here, and the slides here.
- Neural Architecture Search: Insights from 1000 Papers Colin White, Mahmoud Safari, Rhea Sukthanker, Binxin Ru, Thomas Elsken, Arber Zela, Debadeepta Dey, Frank Hutter Preprint. [paper]
- 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 Tackling Climate Change with Machine Learning Workshop at NeurIPS 2022 [paper] [code]
- On the Importance of Architectures and Hyperparameters for Fairness in Face Recognition Rhea Sukthanker*, Samuel Dooley*, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum Meta-Learning Workshop at NeurIPS 2022 [paper]
- Speeding up NAS with Adaptive Subset Selection Vishak Prasad, Colin White, Paarth Jain, Sibasis Nayak, Rishabh Iyer, Ganesh Ramakrishnan Workshop at AutoML 2022 [paper] [1 min video] [5 min video]
- 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 Datasets Track (NeurIPS Datasets Track) 2022 [paper] [code]
- On the Generalizability and Predictability of Recommender Systems Duncan McElfresh*, Sujay Khandagale*, Jonathan Valverde*, John P. Dickerson, Colin White Neural Information Processing Systems (NeurIPS) 2022 [paper] [code] [1 min video] [5 min video]
- 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 Blog Post Track (ICLR Blog Post Track) 2022 [blog post]
- NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy Yash Mehta*, Colin White*, Arber Zela, Arjun Krishnakumar, Guri Zabergja, Shakiba Moradian, Mahmoud Safari, Kaicheng Yu, Frank Hutter International Conference on Learning Representations (ICLR) 2022 [paper] [code] [slides]
- Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu*, Sujay Khandagale*, Colin White, Willie Neiswanger Neural Information Processing Systems Datasets Track (NeurIPS Datasets Track) 2021 [paper] [code] [6 min video]
- How Powerful are Performance Predictors in Neural Architecture Search? Colin White, Arber Zela, Binxin Ru, Yang Liu, Frank Hutter Selected for a contributed talk at the NAS@ICLR Workshop 2021 Neural Information Processing Systems (NeurIPS) 2021 [paper] [code] [slides] [2 min video] [15 min video]
- NAS-Bench-x11 and the Power of Learning Curves Shen Yan*, Colin White*, Yash Savani, Frank Hutter Neural Information Processing Systems (NeurIPS) 2021 [paper] [code] [slides] [15 min video]
- Exploring the Loss Landscape in Neural Architecture Search Colin White, Sam Nolen, Yash Savani Uncertainty in Artificial Intelligence (UAI) 2021 [paper] [code] [blog post] [slides] [8 min video]
- BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search Colin White, Willie Neiswanger, Yash Savani AAAI Conference on Artificial Intelligence (AAAI) 2021 [paper] [code] [blog post] [slides] [18 min video]
- A Study on Encodings for Neural Architecture Search Colin White, Willie Neiswanger, Sam Nolen, Yash Savani Selected for spotlight presentation Neural Information Processing Systems (NeurIPS) 2020 [paper] [code] [3 min video] [10 min video]
- Intra-Processing Methods for Debiasing Neural Networks Yash Savani, Colin White, Naveen Govindarajulu Neural Information Processing Systems (NeurIPS) 2020 [paper] [code] [blog post] [3 min video]
- k-center Clustering under Perturbation Resilience With Maria-Florina Balcan and Nika Haghtalab Transactions on Algorithms Journal (TALG) 2020 Extends results from ICALP 2016 and this arXiv preprint [paper]
- Robust Communication-Optimal Distributed Clustering Algorithms With Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan, and David Woodruff International Colloquium on Automata, Languages, and Programming (ICALP) 2019 [paper]
- New Aspects of Beyond Worst-Case Analysis Colin White Ph.D. Thesis, Carnegie Mellon University, 2018 [paper]
- Data-Driven Clustering via Parameterized Lloyd's Families With Maria-Florina Balcan and Travis Dick Selected for spotlight presentation Neural Information Processing Systems (NeurIPS) 2018 [paper]
- Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems With Maria-Florina Balcan, Vaishnavh Nagarajan, and Ellen Vitercik Conference on Learning Theory (COLT) 2017 [paper] [10 min video]
- Data Driven Resource Allocation for Distributed Learning With Travis Dick, Mu Li, Krishna Pillutla, Maria-Florina Balcan, and Alex Smola International Conference on Artificial Intelligence and Statistics (AISTATS) 2017 [paper]
- Learning Combinatorial Functions from Pairwise Comparisons With Maria-Florina Balcan and Ellen Vitercik Conference on Learning Theory (COLT) 2016 [paper] [10 min video]
- Lower Bounds in the Preprocessing and Query Phases of Routing Algorithms Colin White European Symposium on Algorithms (ESA) 2015 [paper]
- Small dynamical heights for quadratic polynomials and rational functions With Rob Benedetto, Ruqian Chen, Trevor Hyde, and Yordanka Kovacheva Journal of Experimental Mathematics, 2014 [paper]
Program Chair for AutoML 2023.
Local Chair and Area Chair for AutoML 2022.
Co-organizer for the 8th AutoML Workshop at ICML 2021.
Top 10% of reviewers at NeurIPS 2022
Top 10% of reviewers at ICML 2022
Top 10% of reviewers at ICLR 2022
Top 10% of reviewers at NeurIPS 2021
Top 10% of reviewers at ICML 2021
Top 10% of reviewers at NeurIPS 2020
Top 50% of reviewers at NeurIPS 2019