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I'm a first-year Ph.D. student in Computer Science at the University of Texas, 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 data privacy and log-concave sampling. More broadly, I'm interested in problems at the intersection of theoretical computer science, high-dimensional statistics, probability theory, and machine learning. Email / CV / Google Scholar / Twitter
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anminggu@cs.utexas.edu
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Authors are ordered alphabetically unless starred. Then, authors are in contribution order with starred authors contributing equally. Functional Stochastic LocalizationAnming Gu, Bobby Shi, Kevin Tian arXiv Differentially Private Wasserstein Barycenters Anming Gu*, Sasidhar Kunapuli, Edward Chien, Mark Bun, Kristjan Greenewald arXiv Mirror Mean-Field Langevin Dynamics Anming Gu, Juno Kim 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 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 |