I am Ruomin Huang (黄若民 in Chinese), a 2nd-year CS Ph.D. student at Duke University. I am very fortunate to be advised by Prof. Rong Ge. Previously I received M.S. in Data Science and B.S. in Computational Mathematics from USTC, where I worked with Prof. Hu Ding.

Research

Previously I worked on the algorithmic aspect of ML. Now I am interested in mechanisms physics of transformers.

Publications

* denotes equal contribution.

• Task Descriptors Help Transformers Learn Linear Models In-Context
  Ruomin Huang and Rong Ge
  ICLR 2025

• ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods
  Roy Xie, Junlin Wang, Ruomin Huang, Minxing Zhang, Rong Ge, Jian Pei, Neil Gong, Bhuwan Dhingra
  EMNLP 2024

• An Effective Dynamic Gradient Calibration Method for Continual Learning
  Weichen Lin, Jiaxiang Chen, Ruomin Huang, Hu Ding
  ICML 2024

• Coresets for Wasserstein Distributionally Robust Optimization Problems
  Ruomin Huang, Jiawei Huang, Wenjie Liu, Hu Ding
  NeurIPS 2022 (spotlight)

• Coresets for Relational Data and The Applications
  Jiaxiang Chen, Qingyuan Yang, Ruomin Huang, Hu Ding
  NeurIPS 2022 (spotlight)

• A Novel Sequential Coreset Method for Gradient Descent Algorithms
  Jiawei Huang*, Ruomin Huang*, Wenjie Liu*, Nikolaos M. Freris, Hu Ding
  ICML 2021 (spotlight)

• Randomized Greedy Algorithms and Composable Coreset for k-Center Clustering with Outliers
  Hu Ding, Ruomin Huang, Kai Liu, Haikuo Yu, Zixiu Wang
  Preprint

Teaching Assistant

Undergraduate course

• 01118601 Algorithms for Big Data (2021 spring, USTC)
• COMPSCI 330 Design and Analysis of Algorithms (2024 spring, Duke)

Doctoral course

• COMP7102P01 Advanced Algorithms Design and Analysis (2021 fall, USTC)

Seminar

I am open to discussions.

• Feature Learning Seminar (2022 spring) on papers:
  • the Tensor Program series, by Greg Yang, et al.
• High-Dimensional Statistics Seminar (2021 fall) on books:
  • High-Dimensional Statistics: A Non-Asymptotic Viewpoint, by Martin J. Wainwright.
  • High-Dimensional Probability: An Introduction with Applications in Data Science, by Roman Vershynin.
• Spectral and Algebraic Graph Seminar (2021 fall) on the note:
  • Spectral and Algebraic Graph Theory, by Daniel A. Spielman.