Anming Gu
I'm a first-year Ph.D. student in Computer Science at the University of Texas, Austin, advised by Kevin Tian. Previously, I completed my B.A. in Computer Science at Boston University, where I was fortunate to work with Edward Chien and Kristjan Greenewald on applied optimal transport for machine learning.
I'm interested in optimal transport and its applications to log-concave sampling, robust statistics, and differential privacy. More broadly, I'm interested in problems at the intersection of probability theory, theoretical computer science, and machine learning.
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News
2025.08 Started my PhD at UT Austin
2025.04 Attended ICLR 2025 in Singapore
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anminggu at utexas dot edu
GDC 4.718C
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Publications
(α-β) denotes alphabetical order, * denotes equal contribution
Differentially Private Wasserstein Barycenters
Anming Gu, Sasidhar Kunapuli, Edward Chien, Mark Bun, Kristjan Greenewald
In submission
Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics
Anming Gu, Edward Chien, Kristjan Greenewald
In submission
arXiv
Mirror Mean-Field Langevin Dynamics
Anming Gu*, Juno Kim*
In submission
arXiv
Compute-Optimal LLMs Provably Generalize Better with Scale
Marc Anton Finzi, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J Zico Kolter, Andrew Gordon Wilson
International Conference on Learning Representations, 2025.
arXiv
Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior
Anming Gu, Edward Chien, Kristjan Greenewald
International Conference on Learning Representations, 2025.
Preliminary version in OPT Workshop on Optimization for Machine Learning, 2024. [link]
arXiv / poster / code
k-Mixup Regularization for Deep Learning via Optimal Transport
Kristjan Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien
Transactions on Machine Learning Research, 2023.
arXiv / code
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