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References

The scientific literature this package is based on. jax-ibart is an independent reimplementation; please cite the original iBART papers and the relevant method papers below.

iBART and operator-induced structure (the method)

  1. C.-Y. Liu, S. Ye, M. Li, T. P. Senftle. "A rapid feature selection method for catalyst design: Iterative Bayesian additive regression trees (iBART)." The Journal of Chemical Physics 156, 164105 (2022). doi:10.1063/5.0090055
  2. S. Ye, T. P. Senftle, M. Li. "Operator-induced structural variable selection for identifying materials genes." Journal of the American Statistical Association 119(545):81-94 (2024). arXiv:2110.10195. doi:10.1080/01621459.2023.2294527

Bayesian Additive Regression Trees (the selection engine)

  1. H. A. Chipman, E. I. George, R. E. McCulloch. "BART: Bayesian additive regression trees." The Annals of Applied Statistics 4(1):266-298 (2010).
  2. J. Bleich, A. Kapelner, E. I. George, S. T. Jensen. "Variable selection for BART: An application to gene regulation." The Annals of Applied Statistics 8(3):1750-1781 (2014). (The Global-SE permutation threshold used here.)
  3. G. Petrillo. "Very fast Bayesian Additive Regression Trees on GPU." arXiv:2410.23244 (2024). (The bartz library used by the faithful backend.)

Descriptor / materials-genome context and baselines

  1. L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, M. Scheffler. "Big data of materials science: critical role of the descriptor." Physical Review Letters 114, 105503 (2015).
  2. R. Ouyang, S. Curtarolo, E. Ahmetcik, M. Scheffler, L. M. Ghiringhelli. "SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates." Physical Review Materials 2, 083802 (2018). (The main baseline; see iBART vs SISSO.)

Differentiable selection (the diff backend)

  1. C. Louizos, M. Welling, D. P. Kingma. "Learning Sparse Neural Networks through L0 Regularization." ICLR (2018). arXiv:1712.01312. (The hard-concrete gates used by DiffSelector.)

Uncertainty quantification

  1. V. Vovk, A. Gammerman, G. Shafer. "Algorithmic Learning in a Random World." Springer (2005). (Conformal prediction.)
  2. J. Lei, M. G'Sell, A. Rinaldo, R. J. Tibshirani, L. Wasserman. "Distribution-Free Predictive Inference for Regression." Journal of the American Statistical Association 113(523):1094-1111 (2018). (Split-conformal regression, the basis of conformal_intervals.)
  3. A. N. Angelopoulos, S. Bates. "Conformal Prediction: A Gentle Introduction." Foundations and Trends in Machine Learning (2023).

Dynamical-systems discovery (SINDy and DAEs)

  1. S. L. Brunton, J. L. Proctor, J. N. Kutz. "Discovering governing equations from data by sparse identification of nonlinear dynamical systems (SINDy)." PNAS 113(15):3932-3937 (2016). (Basis of discover_ode.)
  2. N. M. Mangan, S. L. Brunton, J. L. Proctor, J. N. Kutz. "Inferring biological networks by sparse identification of nonlinear dynamics." IEEE Transactions on Molecular, Biological and Multi-Scale Communications 2(1):52-63 (2016). (Implicit-SINDy; foundation for the DAE algebraic part.)
  3. K. Kaheman, J. N. Kutz, S. L. Brunton. "SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics." Proceedings of the Royal Society A 476:20200279 (2020). (Implicit / rational / DAE identification.)
  4. D. A. Messenger, D. M. Bortz. "Weak SINDy for partial differential equations." Journal of Computational Physics 443:110525 (2021). (Weak/integral formulation; recommended for noisy DAE discovery.)
  5. R. Chartrand. "Numerical differentiation of noisy, nonsmooth data." ISRN Applied Mathematics (2011). (Robust derivative estimation for SINDy.)