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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 Ed Chien and Kristjan Greenewald on optimal transport for machine learning. I work on problems at the intersection of theoretical computer science, high-dimensional statistics, and machine learning, with a focus on sampling and data privacy. Recently, I've been thinking about bridging the gap between theory and practice in generative AI. 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!CV / Google Scholar / LinkedIn / Twitter
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anminggu@cs.utexas.edu
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Authors are ordered alphabetically unless they are not.
Polytope Sampling at the Speed of Isoperimetry
Anming Gu, Bobby Shi, Kevin Tian, Hubert Yang [arXiv]
Gap-Free Streaming PCA Beyond Rank-One Updates: Near-Optimal Rates and Applications to Differential Privacy
Anming Gu, Syamantak Kumar, Kevin Tian, Chutong Yang [arXiv]
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] No selected publications yet. |