Publications

• 2024

  • Principal Component Analysis in Space Forms
    Puoya Tabaghi, Michael Khanzadeh, Yusu Wang, and Siavash Mirarab. IEEE Transactions on Signal Processing (TSP).

  • Universal Representation of Permutation-Invariant Functions on Vectors and Tensors
    Puoya Tabaghi and Yusu Wang. Proceedings of the 35th International Conference on Algorithmic Learning Theory (ALT).

  • DE-HNN: An Effective Neural Model for Circuit Netlist Representation
    Zhishang Luo, Truong Son Hy, Puoya Tabaghi, Michael Defferrard, Elahe Rezaei, Ryan Carey, Rhett Davis, Rajeev Jain, and Yusu Wang. Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:4258-4266.

  • Learning Ultrametric Trees for Optimal Transport Regressions
    Samantha Chen, Puoya Tabaghi, and Yusu Wang. Proceedings of the AAAI Conference on Artificial Intelligence, 38(18), 20657-20665.

  • Optimal Tree Metric Matching Enables Phylogenomic Branch Length Reconciliation
    Shayesteh Arasti, Puoya Tabaghi (equal contributions), Yasamin Tabatabaee, and Siavash Mirarab. Research in Computational Molecular Biology Conference (RECOMB).

• 2022

  • Learning Hyperbolic Embedding for Phylogenetic Tree Placement and Updates
    Yueyu Jiang, Puoya Tabaghi, Siavash Mirarab. Biology 2022, 11, 1256.

  • HyperAid: Denoising in Hyperbolic Spaces for Tree-Fitting and Hierarchical Clustering
    Eli Chien, Puoya Tabaghi, Olgica Milenkovic. Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

  • Phylogenetic Placement Problem: A Hyperbolic Embedding Approach
    Yueyu Jiang, Puoya Tabaghi, Siavash Mirarab. The 19th Annual Satellite Conference of RECOMB on Comparative Genomics (RECOMB-CG).

• 2021

  • Highly Scalable and Provably Accurate Classification in Poincaré Balls
    Eli Chien, Chao Pan, Puoya Tabaghi, and Olgica Milenkovic. IEEE International Conference on Data Mining (ICDM).

  • Geometry of Similarity Comparisons
    Puoya Tabaghi, Jianhao Peng, Olgica Milenkovic, and Ivan Dokmanic. arXiv preprint: 2006.09858.
    

  • On Procrustes Analysis in Hyperbolic Space
    Puoya Tabaghi and Ivan Dokmanic. IEEE Signal Processing Letters.
    

  • Linear Classifiers in Product Space Forms
    Puoya Tabaghi, Chao Pan, Eli Chien, Jianhao Peng, and Olgica Milenkovic. Submitted to Journal of Machine Learning Research (JMLR).
    

• 2020

  • Hyperbolic Distance Matrices
    Puoya Tabaghi and Ivan Dokmanic. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
    

• 2019

  • Kinetic Euclidean Distance Matrices
    Puoya Tabaghi, Ivan Dokmanic, and Martin Vetterli. IEEE Transactions on Signal Processing, Vol. 68, pp. 452-465.
    

  • On the Move: Localization with Kinetic Euclidean Distance Matrices
    Puoya Tabaghi, Ivan Dokmanic, and Martin Vetterli. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
    

  • Learning Schatten-von Neumann Operators
    Puoya Tabaghi, Maarten V. de Hoop, and Ivan Dokmanic. arXiv preprint: 1901.10076.
    

  • Real Polynomial Gram Matrices Without Real Spectral Factors
    Puoya Tabaghi and Ivan Dokmanic. arXiv preprint: 1903.04085.
    

• 2015

  • Class-Preserving Manifold Learning for Detection and Classification
    Puoya Tabaghi and Mahmood R. Azimi-Sadjadi. International Joint Conference on Neural Networks (IJCNN).