Sotabase

Career

· Assistant Professor, University of Southern California2024–
· Postdoctoral Scholar, Computer Science Department, Stanford University2020–2023
· Ph.D. in Machine Learning, Carnegie Mellon University2012–2019
· Bachelor of Science, Columbia University2008–2012
· Assistant Professor, USC

Publications (157)

Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Neural Information Processing Systems · 2018
646
cited
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
AAAI Conference on Artificial Intelligence · 2019
393
cited
Asymptotically Exact, Embarrassingly Parallel MCMC
Conference on Uncertainty in Artificial Intelligence · 2013
336
cited
LiveBench: A Challenging, Contamination-Free LLM Benchmark
arXiv.org · 2024
243
cited
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning
USENIX Symposium on Operating Systems Design and Implementation · 2020
238
cited
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Journal of machine learning research · 2019
190
cited
Methods for comparing uncertainty quantifications for material property predictions
Machine Learning: Science and Technology · 2019
182
cited
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
International Conference on Artificial Intelligence and Statistics · 2019
140
cited
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
Conference on Empirical Methods in Natural Language Processing · 2022
116
cited
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
Neural Information Processing Systems · 2020
114
cited
LLM360: Towards Fully Transparent Open-Source LLMs
arXiv.org · 2023
97
cited
LiveBench: A Challenging, Contamination-Limited LLM Benchmark
International Conference on Learning Representations · 2024
87
cited
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
arXiv.org · 2021
84
cited
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning
NeurIPS Datasets and Benchmarks · 2021
83
cited
A Study on Encodings for Neural Architecture Search
Neural Information Processing Systems · 2020
81
cited
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
International Conference on Learning Representations · 2020
75
cited
SlimPajama-DC: Understanding Data Combinations for LLM Training
arXiv.org · 2023
74
cited
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions
arXiv.org · 2024
74
cited
Offline Contextual Bayesian Optimization
Neural Information Processing Systems · 2019
53
cited
Fast Distribution To Real Regression
International Conference on Artificial Intelligence and Statistics · 2013
44
cited
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