HTorch: PyTorch-based Robust Optimization in Hyperbolic Space

Install

Requirements

Python >= 3.6 PyTorch >= 1.10.0 CUDA >= 10.1 on linux

Procedures

1. clone this repo locally
2. run pip install ..

We provide examples using HTorch for optimization in hyperbolic space in examples folder, please check it out.

Authors

• Tao Yu, tyu@cs.cornell.edu
• Wentao Guo, wg247@cornell.edu
• Jianan Canal Li, jl3789@cornell.edu
• Tiancheng Yuan, ty373@cornell.edu
• Christopher De Sa, cdesa@cs.cornell.edu

Cite us

If you find HTorch helpful in your research, please consider citing us:

@misc{ydtydr/HTorch,
  author = {Yu, Tao and Guo, Wentao and Li, Jianan Canal and Yuan, Tiancheng and De Sa, Christopher},
  title = {HTorch: PyTorch-based Robust Optimization in Hyperbolic Space},
  year = {2023},
  howpublished = {GitHub Repository},
  url = {https://github.com/ydtydr/HTorch}
}