Viet-Hoang Tran

I'm a final-year Ph.D. student in the Department of Mathematics at the National University of Singapore (expected to graduate this October).

I began my Ph.D. in August 2022 in Mathematics. In September 2023, I shifted my research focus to Machine Learning and started working under the supervision of Professor Tan Minh Nguyen.

I completed an Honors degree in Mathematics at the Hanoi University of Science (2018-2022).

email: hoang.tranviet [at] u.nus.edu | Google Scholar | CV

Research Interests

• I study symmetries in neural networks, which boils down to one question: Given a model \(f\), if two parameter settings \(\theta\) and \(\bar{\theta}\) represent the same function, i.e., \(f_{\theta} \equiv f_{\bar{\theta}}\), how are \(\theta\) and \(\bar{\theta}\) related? These symmetries have all sorts of applications, including understanding implicit bias through neural conservation laws, symmetry-aware optimization and quantization, equivariant metanetworks, mode connectivity up to symmetry, and more.

• Recently, I've also become interested in using AI to tackle math problems, mostly related to commutative algebra and algebraic geometry. Partly for fun and curiosity, but more importantly, as a way to better understand what LLMs can and can't do in mathematical reasoning. I share my thoughts and progress here.

News

• Sep 2026. 2 papers accepted at NeurIPS 2026:

  • Tree-Sliced Orlicz Integral Probability Metric

  • Conformal Prediction for Distribution-to-Distribution Regression

• Aug 2026. Amid the Math-AI hype, with help from models available through ChatGPT and Claude Code and an agentic harness we built around them, I started exploring a few maths problems and got some decent results. The goal is to better understand what LLMs can and can't do in mathematical reasoning, rather than simply to get the problems solved. I share my progress on this project page.

• May 2026. 3 papers accepted at ICML 2026:

  • Conservation Laws for Modern Neural Architectures (spotlight)

  • Functional Equivalence in Attention: A Comprehensive Study with Applications to Linear Mode Connectivity

  • Geometric and Stochastic Analysis of Discontinuities in Sparse Mixture-of-Experts (spotlight)

• Jan 2026. 4 papers accepted at ICLR 2026:

  • Quasi-Equivariant Metanetworks

  • Mixed-Curvature Tree-Sliced Wasserstein Distance

  • Tree-sliced Sobolev IPM

  • Revisiting Tree-Sliced Wasserstein Distance Through the Lens of the Fermat-Weber Problem

Selected Publications

• Conservation Laws for Modern Neural Architectures
  Viet-Hoang Tran¹, Vinh Khanh Bui¹, Tan Lai Ngoc, Nam Nguyen, Tuan Dam, Tan M. Nguyen²
  ICML 2026 (spotlight)

• On Linear Mode Connectivity of Mixture-of-Experts Architectures
  Viet-Hoang Tran¹, Van Hoan Trinh¹, Khanh Vinh Bui¹, Tan M. Nguyen²
  NeurIPS 2025 (oral)

• Equivariant Neural Functional Networks for Transformers
  Viet-Hoang Tran¹, Thieu N. Vo¹, An Nguyen The¹, Tho Tran Huu, Minh-Khoi Nguyen-Nhat, Thanh Tran, Duy-Tung Pham, Tan Minh Nguyen²
  ICLR 2025
  

• Distance-Based Tree-Sliced Wasserstein Distance
  Viet-Hoang Tran¹, Khoi N.M. Nguyen¹, Trang Pham, Thanh T. Chu, Tam Le², Tan M. Nguyen²
  ICLR 2025
  

> all publications