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Anming Gu
I'm a second-year Ph.D. student in Computer Science at UT Austin, where I am fortunate to be advised by Kevin Tian. Previously, I completed my B.A. in Computer Science at Boston University, where I worked with Edward Chien and Kristjan Greenewald on optimal transport for machine learning.
I primarily work on logconcave sampling and data privacy. More broadly, I'm interested in problems at the intersection of theoretical computer science, high-dimensional statistics, probability theory, and machine learning.
In summer 2026, I interned at A*STAR in Singapore, hosted by Atsushi Nitanda.
Please feel free to reach out if you're interested in my work!
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Twitter
News
| 2026.07 |
New note out on logconcave sampling with inexact scores.
Attended ICML 2026 in Seoul, South Korea.
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| 2026.06 |
Attended summer school on Mathematical Aspects of Data Science at NUS.
Started internship at A*STAR in Singapore, working with Atsushi Nitanda.
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| 2026.05 |
One paper in COLT 2026; one paper in ICML 2026.
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| 2026.02 |
First
paper of my PhD on arXiv!
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| 2025.08 |
Started my PhD at UT Austin. |
| 2025.04 |
Attended ICLR 2025 in Singapore. |
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anminggu@cs.utexas.edu
GDC 4.718C
2317 Speedway
Austin, TX 78712
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Select Publications
Authors are ordered alphabetically unless they are not.
The Tractability Landscape of Sampling with Inexact Scores
Anming Gu, Kevin Tian, Hubert Yang, Yusong Zhu
[arXiv]
Functional Stochastic Localization
Anming Gu, Bobby Shi, Kevin Tian
Conference on Learning Theory (COLT), 2026.
[arXiv] [COLT] [video (Kevin)]
Mirror Mean-Field Langevin Dynamics
Anming Gu, Juno Kim
International Conference on Machine Learning (ICML), 2026.
[arXiv] [ICML]
Differentially Private Wasserstein Barycenters
Anming Gu, Sasidhar Kunapuli, Mark Bun, Edward Chien, Kristjan Greenewald
[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 (ICLR), 2025.
[arXiv] [ICLR]
Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior
Anming Gu, Edward Chien, Kristjan Greenewald
International Conference on Learning Representations (ICLR), 2025.
Preliminary version in NeurIPS OPT workshop, 2024.
[arXiv] [ICLR] [OPT workshop] [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 (TMLR), 2023.
[arXiv] [TMLR] [code]
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