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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News
2026.07 New note out on logconcave sampling with inexact scores.
Attended ICML 2026 in Seoul, South Korea.
2026.06 Attended summer school on Mathematical Aspects of Data Science at NUS.
Started internship at A*STAR in Singapore, working with Atsushi Nitanda.
2026.05 One paper in COLT 2026; one paper in ICML 2026.
2026.02 First paper of my PhD on arXiv!
2025.08 Started my PhD at UT Austin.
2025.04 Attended ICLR 2025 in Singapore.
profile photo

anminggu@cs.utexas.edu
GDC 4.718C
2317 Speedway
Austin, TX 78712

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]

Template from Jon Barron's webpage
Last modified 2026.08