Michael Y. Li
I am a second year Computer Science PhD student at Stanford, where I'm advised by Noah Goodman and Emily Fox.
Previously, I graduated summa cum laude, Phi Beta Kappa, and Tau Beta Pi from Princeton, where I was fortunate to work with Tom Griffiths and Ryan Adams.
This summer, I'll be interning at Microsoft Research in Redmond.
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GitHub /
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Research
I'm broadly interested in probabilistic modeling/inference and understanding large language models. Recently, I've also worked on using language models to automate model discovery for scientific applications.
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Automated Statistical Model Discovery with Language Models
Michael Y. Li, Emily B. Fox, Noah D. Goodman
arxiv preprint, 2024
paper
We propose a language model driven automated statistical model discovery system.
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NAS-X: Neural Adaptive Smoothing via Twisting
Dieterich Lawson* Michael Y. Li*, Scott W. Linderman
NeurIPS, 2023
Advances in Approximate Bayesian Inference, 2023 [Oral Presentation]
paper
website
We introduce a new method for inference and model learning that combines reweighted-wake sleep and smoothing Sequential Monte Carlo. We theoretically analyze the bias and consistency of our method and then apply it to discrete latent variable modeling and fitting mechanistic models of neural dynamics.
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Why think step-by-step? Reasoning emerges from the locality of experience
Ben Prystawski, Michael Y. Li, Noah D. Goodman
NeurIPS, 2023 [Oral Presentation, top 0.5%]
paper
We empirically and theoretically study when chain-of-thought reasoning emerges in large language models.
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Gaussian Process Surrogate Models for Neural Networks
Michael Y. Li, Erin Grant, Thomas L. Griffiths
UAI, 2023
paper
We propose a framework that uses Gaussian processes to approximate neural networks. We use this framework to analyze neural network training dynamics and identify influential data points.
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Learning to Learn Functions
Michael Y. Li, Fred Callaway, William D. Thompson, Ryan P. Adams, Thomas L. Griffiths
Cognitive Science, 2023
paper
We propose hierarchical Bayesian models of how people learn to learn functions and validate our model in behavioral experiments.
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