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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 differential privacy and optimal transport, in particular its applications to log-concave sampling and diffusion models. More broadly, I'm interested in problems at the intersection of probability theory, theoretical computer science, and machine learning. CV / Google Scholar / Twitter
News
2025.08 Started my PhD at UT Austin 2025.04 Attended ICLR 2025 in Singapore |
anminggu [at] utexas [dot] edu
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Authors are ordered alphabetically unless authors are starred. Then, authors are in contribution order with starred authors contributing equally. Differentially Private Wasserstein BarycentersAnming Gu*, Sasidhar Kunapuli, Edward Chien, Mark Bun, Kristjan Greenewald In submission arXiv Mirror Mean-Field Langevin Dynamics Anming Gu, Juno Kim In submission arXiv Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics Anming Gu*, 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 ICLR 2025. arXiv Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior Anming Gu*, Edward Chien, Kristjan Greenewald ICLR 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 TMLR 2023. arXiv / code |