About Me

I’m a fourth-year computer science PhD student in Northwestern University’s theory group. My research interests are in theoretical computer science and the theoretical foundations of machine learning.

Before beginning my PhD studies at Northwestern University, I completed both my undergraduate and master’s degrees at the same institution. (Go Cats!)

Publications and Manuscripts

Manuscripts and Ongoing Projects

Algorithmic Lemma Discovery via Formal Library Compression Ruize Xu, Yi Gu, Anxin Guo, Tianchong Jiang, Chenxiao Yang, and Zhiyuan Li. Manuscript
Copying Under Repetition: From Prefix Matching to Position Hashing Anxin Guo, Tianlong Huang, Chenxiao Yang, and Zhiyuan Li. Manuscript

Publications

Hallucination is a Consequence of Space-Optimality: A Rate-Distortion Theorem for Membership Testing Anxin Guo and Jingwei Li. ICML 2026 Spotlight · top 2.2% arXivconference version

Summary We model the memorization of random, non-inferable facts as a membership testing problem, connecting Bloom-filter-style error metrics with the log-loss behavior of language models. In the sparse-fact regime, we prove a rate-distortion theorem showing that the optimal space-error tradeoff is governed by a KL-divergence frontier. The result gives an information-theoretic explanation for high-confidence hallucinations: under limited capacity, even an optimal model may assign high confidence to some non-facts rather than simply abstaining or forgetting.

Agnostic Learning of Arbitrary ReLU Activation under Gaussian Marginals Anxin Guo and Aravindan Vijayaraghavan. COLT 2025 arXivconference versionrecorded virtual talk

Summary We give the first algorithm for agnostic PAC learning of an arbitrarily biased ReLU neuron under Gaussian input distributions, up to constant approximation. We also prove a hardness separation between SQ (statistical query) and CSQ (correlational statistical query) models for this problem, showing a limitation of gradient-based algorithms in this setting.

To Store or Not to Store: A Graph-Theoretic Approach for Dataset Versioning Anxin Guo, Jingwei Li, Pattara Sukprasert, Samir Khuller, Amol Deshpande, and Koyel Mukherjee. IPDPS 2024 arXivconference version

Summary We study a graph-theoretic framework for dataset versioning that optimizes storage costs while controlling retrieval costs across versions. On the theory side, we prove hardness-of-approximation results and give provably near-optimal algorithms for tree-like graphs of bounded treewidth. These results also lead to practical heuristics with up to 1000x speedups for the "MinSum Retrieval" problem on real-world GitHub repositories.

Education

Ph.D. student in Computer Science
B.A. in Math and M.S. in Computer Science

Awards

Academic Activities

Research and Teaching

Service and Programs

Miscellaneous

I have written three articles on 知乎 about theoretical computer science. Here is the link.

More personal notes

I grew up in Beijing and still have a soft spot for its subway system and shared bicycles.