Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models

Official code for our paper: 'Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models' presented at ICML 2023.

Overall Approach and Alignment Algorithm

Setup

1. Create a virtual environment and activate it
2. Install PyTorch 1.11.0 for your machine and CUDA version from here
3. Install tqdm and matplotlib

Running the Code

• ctreeot.py: implementation of the CTreeOT algorithm
• sinkhorn.py: implementation of the Sinkhorn algorithm
• main.py: runs the experiments comparing the run time and constraint violations of Sinkhorn and CTreeOT on various tree sizes. See run.sh for more details on how to run main.py.
• plot.py: generates the run time and constraint violation plots comparing Sinkhorn and CTreeOT in ./plots. Note that the current plots present inside ./plots were obtained by running the experiments on Google Colab with an NVIDIA T4 GPU, unlike the ones reported in the paper which were on an NVIDIA RTX A6000 GPU.

Citation

If you found our work useful, please cite us as:

@InProceedings{pmlr-v202-varma23a,
  title = 	 {Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models},
  author =       {Varma, Harshit and Awasthi, Abhijeet and Sarawagi, Sunita},
  booktitle = 	 {Proceedings of the 40th International Conference on Machine Learning},
  pages = 	 {34908--34923},
  year = 	 {2023},
  editor = 	 {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan},
  volume = 	 {202},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {23--29 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v202/varma23a/varma23a.pdf},
  url = 	 {https://proceedings.mlr.press/v202/varma23a.html}
}