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Automated machine learning
Data cleaning, feature selection, pipeline design, and hyperparameter tuning remain some of the most challenging (and time-consuming) tasks in data science. My work on automated machine learning (AutoML) seeks to automate these tasks to enable widespread use of machine learning by non-experts.
Talks
Software
Papers
LLMs for Cold-Start Cutting Plane Separator Configuration
C. Lawless, Y. Li, A. Wikum, M. Udell, and E. Vitercik
2025
[arxiv][url][bib]
LLMs for Cold-Start Cutting Plane Separator Configuration
C. Lawless, Y. Li, A. Wikum, M. Udell, and E. Vitercik
International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2025
[arxiv][url][bib]
Resource-Constrained Neural Architecture Search on Tabular Datasets
C. Yang, G. Bender, H. Liu, P. Kindermans, M. Udell, Y. Lu, Q. Le, and D. Huang
NeurIPS, 2022
[arxiv][url][bib]
How Low Can We Go: Trading Memory for Error in Low-Precision Training
C. Yang, Z. Wu, J. Chee, C. D. Sa, and M. Udell
International Conference on Learning Representations (ICLR), 2022
[arxiv][url][bib]
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
W. T. Stephenson, Z. Frangella, M. Udell, and T. Broderick
Advances in Neural Information Processing Systems (NeurIPS), 2021
[arxiv][bib]
Privileged Zero-Shot AutoML
N. Singh, B. Kates, J. Mentch, A. Kharkar, M. Udell, and I. Drori
2021
[arxiv][url][bib]
Efficient AutoML Pipeline Search with Matrix and Tensor Factorization
C. Yang, J. Fan, Z. Wu, and M. Udell
2020
[arxiv][url][bib]
Real-time AutoML
I. Drori, L. Liu, Q. Ma, J. Deykin, B. Kates, and M. Udell
2020
[url][bib]
AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space
C. Yang, J. Fan, Z. Wu, and M. Udell
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020
[arxiv][url][bib]
AutoML using Metadata Language Embeddings
I. Drori, L. Liu, S. Koorathota, N. Yi, J. Li, A. Moretti, J. Freire, and M. Udell
NeurIPS Workshop on Meta-Learning, 2019
[arxiv][pdf][bib]
OBOE: Collaborative Filtering for AutoML Model Selection
C. Yang, Y. Akimoto, D. Kim, and M. Udell
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019
Oral presentation
[arxiv][pdf][url][bib][video]
OBOE: Collaborative Filtering for AutoML Initialization (workshop version)
C. Yang, Y. Akimoto, D. Kim, and M. Udell
NeurIPS Workshop on Automated Machine Learning, 2018
[arxiv][pdf][url][bib]
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