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I'm a first-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. 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. Please feel free to reach out if you're interested in my work! CV / Google Scholar / Twitter
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anminggu@cs.utexas.edu
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Authors are ordered alphabetically unless they are not. The Tractability Landscape of Sampling with Inexact ScoresAnming Gu, Kevin Tian, Hubert Yang, Yusong Zhu arXiv Functional Stochastic Localization Anming Gu, Bobby Shi, Kevin Tian Conference on Learning Theory (COLT), 2026. arXiv Mirror Mean-Field Langevin Dynamics Anming Gu, Juno Kim International Conference on Machine Learning (ICML), 2026. arXiv 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 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 / 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 / code |