% Generated by catalog/scripts/generate_bibliography.py; do not edit.
% Legacy entries are lossless import notes, not reviewed bibliographic
% record. Its title is the legacy method title and its note preserves the
% historical citation string; no authors, venue, year, DOI, or URL is inferred.

@misc{legacy-aom_pls,
  title = {AOM-PLS (global adaptive operator selection)},
  howpublished = {Historical method bibliography catalog},
  note = {Beurier, G., Reiter, R., Noûs, C., Rouan, L. \& Cornet, D. (2026). *Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: a large-scale benchmark of operator-adaptive PLS and Ridge models*. arXiv:2605.13587. https://arxiv.org/abs/2605.13587.}
}

@misc{legacy-aom_preprocess,
  title = {AOM (Adaptive Operator Mixture) preprocessing bank},
  howpublished = {Historical method bibliography catalog},
  note = {Beurier, G., Reiter, R., Noûs, C., Rouan, L. \& Cornet, D. (2026). *Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: a large-scale benchmark of operator-adaptive PLS and Ridge models*. arXiv:2605.13587. https://arxiv.org/abs/2605.13587 — introduces operator-adaptive PLS (AOM-PLS / POP-PLS) and the bench against 50+ NIRS datasets that the git-pinned oracle `nirs4all.operators.models.sklearn.aom\_pls` is calibrated against.}
}

@misc{legacy-approximate_press,
  title = {Approximate PRESS (leave-one-out by hat-matrix)},
  howpublished = {Historical method bibliography catalog},
  note = {Allen, D. M. (1974). *The relationship between variable selection and data augmentation and a method for prediction*. Technometrics 16(1), 125–127.}
}

@misc{legacy-bagging_pls,
  title = {Bagging PLS},
  howpublished = {Historical method bibliography catalog},
  note = {Breiman, L. (1996). *Bagging predictors*. Machine Learning 24(2), 123–140. — adapted for PLS by various chemometric authors.}
}

@misc{legacy-bipls_select,
  title = {biPLS — Backward Interval PLS},
  howpublished = {Historical method bibliography catalog},
  note = {Leardi, R. \& Nørgaard, L. (2004). *Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions*. Journal of Chemometrics 18(11), 486–497.}
}

@misc{legacy-boosting_pls,
  title = {Boosting PLS},
  howpublished = {Historical method bibliography catalog},
  note = {Friedman, J. H. (2001). *Greedy function approximation: a gradient boosting machine*. Annals of Statistics 29(5), 1189–1232. — adapted for PLS as a base learner.}
}

@misc{legacy-bve_select,
  title = {BVE — Backward Variable Elimination},
  howpublished = {Historical method bibliography catalog},
  note = {Forina, M., Casolino, M. C. \& Pizarro Millán, C. (2004). *Iterative predictor weighting (IPW) PLS: a technique for the elimination of useless predictors in regression problems*. Journal of Chemometrics 18(2), 105–112 (§2).}
}

@misc{legacy-cars_select,
  title = {CARS — Competitive Adaptive Reweighted Sampling},
  howpublished = {Historical method bibliography catalog},
  note = {Li, H., Liang, Y., Xu, Q. \& Cao, D. (2009). *Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration*. Analytica Chimica Acta 648(1), 77–84.}
}

@misc{legacy-continuum_regression,
  title = {Continuum Regression (Stone \& Brooks 1990)},
  howpublished = {Historical method bibliography catalog},
  note = {Stone, M. \& Brooks, R. J. (1990). *Continuum regression: cross-validated sequentially constructed prediction embracing ordinary least squares, partial least squares and principal components regression*. JRSS B 52(2), 237–269.}
}

@misc{legacy-cppls,
  title = {Powered PLS (Indahl 2005)},
  howpublished = {Historical method bibliography catalog},
  note = {Indahl, U. G. (2005). *A twist to partial least squares regression*. Journal of Chemometrics 19(1), 32–44.}
}

@misc{legacy-di_pls,
  title = {Domain-Invariant PLS (di-PLS)},
  howpublished = {Historical method bibliography catalog},
  note = {Nikzad-Langerodi, R., Zellinger, W., Saminger-Platz, S. \& Moser, B. A. (2018). *Domain-invariant partial-least-squares regression*. Analytical Chemistry 90(11), 6693–6701.}
}

@misc{legacy-ds,
  title = {Direct Standardisation},
  howpublished = {Historical method bibliography catalog},
  note = {Wang, Y., Veltkamp, D. J. \& Kowalski, B. R. (1991). *Multivariate instrument standardisation*. Analytical Chemistry 63(23), 2750–2756.}
}

@misc{legacy-ecr,
  title = {ECR — Elastic Component Regression},
  howpublished = {Historical method bibliography catalog},
  note = {Liu, Y., Zhang, B. \& Hu, J. (2013). *Elastic Component Regression*. Chemometrics and Intelligent Laboratory Systems 124, 73–79. — adapted in pls4all as a continuum/elastic blend.}
}

@misc{legacy-emcuve_select,
  title = {EMCUVE — Ensemble MC-UVE},
  howpublished = {Historical method bibliography catalog},
  note = {Han, Q.-J., Wu, H.-L., Cai, C.-B., Xu, L. \& Yu, R.-Q. (2008). *An ensemble of Monte Carlo uninformative variable elimination for wavelength selection*. Analytica Chimica Acta 612(2), 121–125. https://doi.org/10.1016/j.aca.2008.02.032 — extends the MC-UVE procedure of Cai et al. (2008) (`stability\_select`) by aggregating independent MC-UVE rounds through a vote rule.}
}

@misc{legacy-fused_sparse_pls,
  title = {Fused-sparse PLS},
  howpublished = {Historical method bibliography catalog},
  note = {Tibshirani, R., Saunders, M., Rosset, S., Zhu, J. \& Knight, K. (2005). *Sparsity and smoothness via the fused lasso*. JRSS B 67(1), 91–108. — generalised to PLS loadings.}
}

@misc{legacy-ga_select,
  title = {GA-PLS — Genetic Algorithm variable selection},
  howpublished = {Historical method bibliography catalog},
  note = {Leardi, R. (2000). *Application of genetic algorithm–PLS for feature selection in spectral data sets*. Journal of Chemometrics 14(5–6), 643–655.}
}

@misc{legacy-gpr_pls,
  title = {Gaussian Process on PLS scores},
  howpublished = {Historical method bibliography catalog},
  note = {Bishop, C. M. (2006). *Pattern Recognition and Machine Learning*, §6.4 (Gaussian Processes). — combined with a preliminary PLS dimensionality reduction for spectroscopy.}
}

@misc{legacy-group_sparse_pls,
  title = {Group-sparse PLS (Liquet 2016)},
  howpublished = {Historical method bibliography catalog},
  note = {Liquet, B., de Micheaux, P. L., Hejblum, B. P. \& Thiébaut, R. (2016). *Group and sparse group partial least squares approaches applied in genomics context*. Bioinformatics 32(1), 35–42.}
}

@misc{legacy-interval_select,
  title = {iPLS — Interval PLS (moving-window)},
  howpublished = {Historical method bibliography catalog},
  note = {Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J. P., Munck, L. \& Engelsen, S. B. (2000). *Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy*. Applied Spectroscopy 54(3), 413–419.}
}

@misc{legacy-ipw_select,
  title = {IPW — Iterative Predictor Weighting},
  howpublished = {Historical method bibliography catalog},
  note = {Forina, M., Casolino, C. \& Pizarro Millán, C. (1999). *Iterative predictor weighting (IPW) PLS: a technique for the elimination of useless predictors in regression problems*. Journal of Chemometrics 13(2), 165–184. https://doi.org/10.1002/(SICI)1099-128X(199903/04)13:2<165::AID-CEM535>3.0.CO;2-Y}
}

@misc{legacy-irf_select,
  title = {IRF — Iterative Random Forest},
  howpublished = {Historical method bibliography catalog},
  note = {Basu, S., Kumbier, K., Brown, J. B. \& Yu, B. (2018). *Iterative random forests to discover predictive and stable high-order interactions*. Proceedings of the National Academy of Sciences 115(8), 1943–1948.}
}

@misc{legacy-iriv_select,
  title = {IRIV — Iteratively Retaining Informative Variables},
  howpublished = {Historical method bibliography catalog},
  note = {Yun, Y. H., Wang, W. T., Tan, M. L., Liang, Y. Z., Li, H. D., Cao, D. S., Lu, H. M. \& Xu, Q. S. (2014). *A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration*. Analytica Chimica Acta 807, 36–43.}
}

@misc{legacy-kernel_pls_rbf,
  title = {Kernel PLS (Rosipal \& Trejo 2001)},
  howpublished = {Historical method bibliography catalog},
  note = {Rosipal, R. \& Trejo, L. J. (2001). *Kernel partial least squares regression in reproducing kernel Hilbert space*. Journal of Machine Learning Research 2, 97–123.}
}

@misc{legacy-lw_pls,
  title = {Locally-Weighted PLS (LW-PLS)},
  howpublished = {Historical method bibliography catalog},
  note = {Centner, V. \& Massart, D. L. (1998). *Optimisation in locally weighted regression*. Analytical Chemistry 70(19), 4206–4211.}
}

@misc{legacy-mb_pls,
  title = {Multi-block PLS (Westerhuis 1998)},
  howpublished = {Historical method bibliography catalog},
  note = {Westerhuis, J. A., Kourti, T. \& MacGregor, J. F. (1998). *Analysis of multiblock and hierarchical PCA and PLS models*. Journal of Chemometrics 12(5), 301–321.}
}

@misc{legacy-mir_pls,
  title = {MIR-PLS (Mid-InfraRed PLS, regularised)},
  howpublished = {Historical method bibliography catalog},
  note = {Sjöblom, J., Svensson, O., Josefson, M., Kullberg, H. \& Wold, S. (1998). *An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra*. Chemometrics and Intelligent Laboratory Systems 44(1–2), 229–244. — adapted for MIR regularisation conventions.}
}

@misc{legacy-missing_aware_nipals,
  title = {Missing-aware NIPALS},
  howpublished = {Historical method bibliography catalog},
  note = {Walczak, B. \& Massart, D. L. (2001). *Dealing with missing data: part I \& II*. Chemometrics and Intelligent Laboratory Systems 58(1), 15–27 \& 29–42. — applied to the NIPALS PLS algorithm.}
}

@misc{legacy-n_pls,
  title = {N-way PLS (Trilinear PLS, Bro 1996)},
  howpublished = {Historical method bibliography catalog},
  note = {Bro, R. (1996). *Multiway calibration. Multilinear PLS*. Journal of Chemometrics 10(1), 47–61.}
}

@misc{legacy-o2pls,
  title = {O2-PLS (two-way orthogonal)},
  howpublished = {Historical method bibliography catalog},
  note = {Trygg, J. \& Wold, S. (2003). *O2-PLS, a two-block (X–Y) latent variable regression method with an integral OSC filter*. Journal of Chemometrics 17(1), 53–64.}
}

@misc{legacy-on_pls,
  title = {OnPLS (Orthogonal N-block PLS)},
  howpublished = {Historical method bibliography catalog},
  note = {Löfstedt, T. \& Trygg, J. (2011). *OnPLS — a novel multiblock method for the modelling of predictive and orthogonal variation*. Journal of Chemometrics 25(8), 441–455.}
}

@misc{legacy-one_se_rule,
  title = {One-SE rule for component selection},
  howpublished = {Historical method bibliography catalog},
  note = {Hastie, T., Tibshirani, R. \& Friedman, J. (2009). *The Elements of Statistical Learning*, 2nd ed., Springer, §7.10.}
}

@misc{legacy-opls,
  title = {Orthogonal PLS (OPLS)},
  howpublished = {Historical method bibliography catalog},
  note = {Trygg, J. \& Wold, S. (2002). *Orthogonal projections to latent structures (O-PLS)*. Journal of Chemometrics 16(3), 119–128.}
}

@misc{legacy-pcr,
  title = {Principal Components Regression},
  howpublished = {Historical method bibliography catalog},
  note = {Massy, W. F. (1965). *Principal Components Regression in Exploratory Statistical Research*. JASA 60(309), 234–256.}
}

@misc{legacy-pds,
  title = {Piecewise Direct Standardisation},
  howpublished = {Historical method bibliography catalog},
  note = {Wang, Y., Veltkamp, D. J. \& Kowalski, B. R. (1991). *Multivariate instrument standardization*. Analytical Chemistry 63(23), 2750–2756. https://doi.org/10.1021/ac00023a016 — same paper as `ds`; PDS is introduced in §3 (piecewise local regression with a sliding window of width 2w+1).}
}

@misc{legacy-pls,
  title = {PLS regression (SIMPLS)},
  howpublished = {Historical method bibliography catalog},
  note = {de Jong, S. (1993). *SIMPLS: an alternative approach to partial least squares regression*. Chemometrics and Intelligent Laboratory Systems 18(3), 251–263.}
}

@misc{legacy-pls_cox,
  title = {PLS-Cox (survival regression)},
  howpublished = {Historical method bibliography catalog},
  note = {Bastien, P., Bertrand, F., Meyer, N. \& Maumy-Bertrand, M. (2015). *Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data*. Bioinformatics 31(3), 397–404.}
}

@misc{legacy-pls_diagnostic_dmodx,
  title = {DModX (distance to the model in X)},
  howpublished = {Historical method bibliography catalog},
  note = {Eriksson, L., Byrne, T., Johansson, E., Trygg, J. \& Vikström, C. (2013). *Multi- and Megavariate Data Analysis. Basic Principles and Applications*, 3rd ed., Umetrics Academy, §4.7.}
}

@misc{legacy-pls_diagnostic_q,
  title = {Q residual (squared prediction error)},
  howpublished = {Historical method bibliography catalog},
  note = {Jackson, J. E. \& Mudholkar, G. S. (1979). *Control procedures for residuals associated with principal component analysis*. Technometrics 21(3), 341–349.}
}

@misc{legacy-pls_diagnostic_t2,
  title = {Hotelling T² score},
  howpublished = {Historical method bibliography catalog},
  note = {Hotelling, H. (1931). *The generalization of Student's ratio*. Annals of Mathematical Statistics 2(3), 360–378. — applied to PLS scores by MacGregor \& Kourti 1995.}
}

@misc{legacy-pls_glm,
  title = {PLS-GLM (Generalised Linear Model PLS)},
  howpublished = {Historical method bibliography catalog},
  note = {Marx, B. D. (1996). *Iteratively reweighted partial least squares estimation for generalized linear regression*. Technometrics 38(4), 374–381.}
}

@misc{legacy-pls_lda,
  title = {PLS-LDA},
  howpublished = {Historical method bibliography catalog},
  note = {Barker, M. \& Rayens, W. (2003). *Partial least squares for discrimination*. Journal of Chemometrics 17(3), 166–173.}
}

@misc{legacy-pls_logistic,
  title = {PLS-logistic regression},
  howpublished = {Historical method bibliography catalog},
  note = {Bastien, P., Esposito Vinzi, V. \& Tenenhaus, M. (2005). *PLS generalised linear regression*. Computational Statistics \& Data Analysis 48(1), 17–46.}
}

@misc{legacy-pls_monitoring,
  title = {PLS monitoring (T² + Q with control limits)},
  howpublished = {Historical method bibliography catalog},
  note = {Kourti, T. \& MacGregor, J. F. (1996). *Multivariate SPC methods for process and product monitoring and control*. Journal of Quality Technology 28(4), 409–428.}
}

@misc{legacy-pls_qda,
  title = {PLS-QDA},
  howpublished = {Historical method bibliography catalog},
  note = {Pérez-Enciso, M. \& Tenenhaus, M. (2003). *Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach*. Human Genetics 112(5–6), 581–592.}
}

@misc{legacy-pop_pls,
  title = {POP-PLS (per-component operator selection)},
  howpublished = {Historical method bibliography catalog},
  note = {Beurier, G., Reiter, R., Noûs, C., Rouan, L. \& Cornet, D. (2026). *Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: a large-scale benchmark of operator-adaptive PLS and Ridge models*. arXiv:2605.13587. https://arxiv.org/abs/2605.13587.}
}

@misc{legacy-pso_select,
  title = {PSO-PLS — Particle Swarm Optimisation},
  howpublished = {Historical method bibliography catalog},
  note = {Kennedy, J. \& Eberhart, R. (1995). *Particle swarm optimization*. IEEE ICNN 1995, vol. 4, 1942–1948. — binary PSO variant used for variable selection.}
}

@misc{legacy-random_frog_select,
  title = {Random Frog},
  howpublished = {Historical method bibliography catalog},
  note = {Li, H., Xu, Q. \& Liang, Y. (2012). *Random frog: an efficient reversible jump Markov chain Monte Carlo-like approach for variable selection*. Analytica Chimica Acta 740, 20–26.}
}

@misc{legacy-random_subspace_pls,
  title = {Random-subspace PLS},
  howpublished = {Historical method bibliography catalog},
  note = {Ho, T. K. (1998). *The random subspace method for constructing decision forests*. IEEE TPAMI 20(8), 832–844. — adapted for PLS regressors.}
}

@misc{legacy-randomization_select,
  title = {Randomisation test (Y-permutation)},
  howpublished = {Historical method bibliography catalog},
  note = {Westad, F. \& Martens, H. (2000). *Variable selection in near infrared spectroscopy based on significance testing in partial least squares regression*. JNIRS 8(2), 117–124.}
}

@misc{legacy-recursive_pls,
  title = {Recursive (moving-window) PLS},
  howpublished = {Historical method bibliography catalog},
  note = {Helland, K., Berntsen, H. E., Borgen, O. S. \& Martens, H. (1992). *Recursive algorithm for partial least squares regression*. Chemometrics and Intelligent Laboratory Systems 14(1–3), 129–137.}
}

@misc{legacy-rep_select,
  title = {REP — Recursive Elimination of Predictors},
  howpublished = {Historical method bibliography catalog},
  note = {Mehmood, T., Liland, K. H., Snipen, L. \& Sæbø, S. (2012). *A review of variable selection methods in partial least squares regression*. Chemometrics and Intelligent Laboratory Systems 118, 62–69. https://doi.org/10.1016/j.chemolab.2012.07.010 — same review as `shaving\_select`; §3.3 *Recursive elimination* introduces the fixed-count variant implemented here.}
}

@misc{legacy-ridge_pls,
  title = {Ridge-augmented PLS},
  howpublished = {Historical method bibliography catalog},
  note = {Hoerl, A. E. \& Kennard, R. W. (1970). *Ridge regression: biased estimation for nonorthogonal problems*. Technometrics 12(1), 55–67. — combined with PLS via Tikhonov regularisation of the inner regression.}
}

@misc{legacy-robust_pls,
  title = {Robust PLS (Partial Robust M-regression)},
  howpublished = {Historical method bibliography catalog},
  note = {Serneels, S., Croux, C., Filzmoser, P. \& Van Espen, P. J. (2005). *Partial Robust M-Regression*. Chemometrics and Intelligent Laboratory Systems 79(1–2), 55–64.}
}

@misc{legacy-rosa,
  title = {ROSA (Response-Oriented Sequential Alternation)},
  howpublished = {Historical method bibliography catalog},
  note = {Liland, K. H. \& Næs, T. (2016). *Response-oriented sequential alternation: a fast multiblock regression algorithm*. Journal of Chemometrics 30(11), 651–662.}
}

@misc{legacy-scars_select,
  title = {SCARS — Stability-CARS},
  howpublished = {Historical method bibliography catalog},
  note = {Zheng, K., Li, Q., Wang, J., Geng, J., Cao, P., Sui, T., Wang, X. \& Du, Y. (2012). *Stability competitive adaptive reweighted sampling (SCARS) and its applications to multivariate calibration of NIR spectra*. Chemometrics and Intelligent Laboratory Systems 112, 48–54.}
}

@misc{legacy-shaving_select,
  title = {Shaving (recursive elimination)},
  howpublished = {Historical method bibliography catalog},
  note = {Mehmood, T., Liland, K. H., Snipen, L. \& Sæbø, S. (2012). *A review of variable selection methods in partial least squares regression*. Chemometrics and Intelligent Laboratory Systems 118, 62–69 (§3.2 Shaving).}
}

@misc{legacy-sipls_select,
  title = {siPLS — Synergy Interval PLS},
  howpublished = {Historical method bibliography catalog},
  note = {Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J. P., Munck, L. \& Engelsen, S. B. (2000). *Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy*. Applied Spectroscopy 54(3), 413–419 — same paper as `interval\_select`; siPLS is the synergy-combinations extension proposed in §3.}
}

@misc{legacy-so_pls,
  title = {Sequential and Orthogonalised PLS (SO-PLS)},
  howpublished = {Historical method bibliography catalog},
  note = {Næs, T., Tomic, O., Mevik, B.-H. \& Martens, H. (2011). *Path modelling by sequential PLS regression*. Journal of Chemometrics 25(1), 28–40.}
}

@misc{legacy-spa_select,
  title = {SPA — Successive Projections Algorithm},
  howpublished = {Historical method bibliography catalog},
  note = {Araújo, M. C. U., Saldanha, T. C. B., Galvão, R. K. H., Yoneyama, T., Chame, H. C. \& Visani, V. (2001). *The successive projections algorithm for variable selection in spectroscopic multicomponent analysis*. Chemometrics and Intelligent Laboratory Systems 57(2), 65–73.}
}

@misc{legacy-sparse_pls_da,
  title = {Sparse PLS-DA (Lê Cao 2008)},
  howpublished = {Historical method bibliography catalog},
  note = {Lê Cao, K.-A., Rossouw, D., Robert-Granié, C. \& Besse, P. (2008). *A sparse PLS for variable selection when integrating omics data*. Statistical Applications in Genetics and Molecular Biology 7(1).}
}

@misc{legacy-sparse_simpls,
  title = {Sparse SIMPLS (Chun \& Keleş 2010)},
  howpublished = {Historical method bibliography catalog},
  note = {Chun, H. \& Keleş, S. (2010). *Sparse partial least squares regression for simultaneous dimension reduction and variable selection*. JRSS B 72(1), 3–25.}
}

@misc{legacy-st_select,
  title = {ST-PLS — Score Threshold selection},
  howpublished = {Historical method bibliography catalog},
  note = {Mehmood, T., Liland, K. H., Snipen, L. \& Sæbø, S. (2012). *A review of variable selection methods in partial least squares regression*. Chemometrics and Intelligent Laboratory Systems 118, 62–69. https://doi.org/10.1016/j.chemolab.2012.07.010 — same review as `shaving\_select`; §3.4 *Score-threshold methods* covers the deterministic-threshold family implemented here.}
}

@misc{legacy-stability_select,
  title = {MC-UVE (Monte-Carlo coefficient stability)},
  howpublished = {Historical method bibliography catalog},
  note = {Cai, W., Li, Y. \& Shao, X. (2008). *A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra*. Chemometrics and Intelligent Laboratory Systems 90(2), 188–194.}
}

@misc{legacy-t2_select,
  title = {Hotelling T² loading selection},
  howpublished = {Historical method bibliography catalog},
  note = {Mehmood, T. (2016). *Hotelling T² based variable selection in partial least squares regression*. Chemometrics and Intelligent Laboratory Systems 154, 23–28. https://doi.org/10.1016/j.chemolab.2016.03.020 — proposes T²-PLS, the loading-weights-level Hotelling T² selector. See also Wold, Sjöström \& Eriksson (2001), Chemometrics and Intelligent Laboratory Systems 58(2), 109–130 §6.2 for the original T²-vs-VIP discussion in PLS.}
}

@misc{legacy-uve_select,
  title = {UVE — Uninformative Variable Elimination},
  howpublished = {Historical method bibliography catalog},
  note = {Centner, V., Massart, D. L., de Noord, O. E., de Jong, S., Vandeginste, B. M. \& Sterna, C. (1996). *Elimination of uninformative variables for multivariate calibration*. Analytical Chemistry 68(21), 3851–3858.}
}

@misc{legacy-variable_select_coef,
  title = {Coefficient-magnitude selection},
  howpublished = {Historical method bibliography catalog},
  note = {Martens, H. \& Næs, T. (1989). *Multivariate Calibration*, §5. — the simplest ranking baseline.}
}

@misc{legacy-variable_select_sr,
  title = {Selectivity Ratio},
  howpublished = {Historical method bibliography catalog},
  note = {Rajalahti, T., Arneberg, R., Berven, F. S., Myhr, K.-M., Ulvik, R. J. \& Kvalheim, O. M. (2009). *Biomarker discovery in mass spectral profiles by means of selectivity ratio plot*. Chemometrics and Intelligent Laboratory Systems 95(1), 35–48.}
}

@misc{legacy-variable_select_vip,
  title = {VIP (Variable Importance in Projection)},
  howpublished = {Historical method bibliography catalog},
  note = {Wold, S., Sjöström, M. \& Eriksson, L. (2001). *PLS-regression: a basic tool of chemometrics*. Chemometrics and Intelligent Laboratory Systems 58(2), 109–130.}
}

@misc{legacy-vip_spa_select,
  title = {VIP-seeded SPA},
  howpublished = {Historical method bibliography catalog},
  note = {Hybrid heuristic combining VIP ranking and the Successive Projections Algorithm. See registry notes; no single canonical paper.}
}

@misc{legacy-vissa_select,
  title = {VISSA — Variable Iterative Space-Shrinkage},
  howpublished = {Historical method bibliography catalog},
  note = {Deng, B. C., Yun, Y. H., Liang, Y. Z. \& Yi, L. Z. (2014). *A new strategy to prevent over-fitting in partial least squares models based on model population analysis*. Analytica Chimica Acta 880, 32–41.}
}

@misc{legacy-weighted_pls,
  title = {Sample-weighted PLS},
  howpublished = {Historical method bibliography catalog},
  note = {Martens, H. \& Næs, T. (1989). *Multivariate Calibration*. Wiley. §4.5 'Weighted regression for non-i.i.d. errors'.}
}

@misc{legacy-wvc_select,
  title = {WVC — Weighted Variable Contribution},
  howpublished = {Historical method bibliography catalog},
  note = {Andries, J. P. M. \& Vander Heyden, Y. (2011). *Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity*. Analytica Chimica Acta 705(1–2), 292–305.}
}

@misc{legacy-wvc_threshold_select,
  title = {WVC-threshold selection},
  howpublished = {Historical method bibliography catalog},
  note = {Andries, J. P. M. \& Vander Heyden, Y. (2011). *Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity*. Analytica Chimica Acta 705(1–2), 292–305. https://doi.org/10.1016/j.aca.2011.06.037 — same paper as `wvc\_select`; introduces both the top-\$k\$ ranking and the threshold / factor-of-mean rules used here.}
}

@inproceedings{bergstra2011tpe,
  author    = {Bergstra, James and Bardenet, R{\'e}mi and Bengio, Yoshua and
               K{\'e}gl, Bal{\'a}zs},
  title     = {Algorithms for Hyper-Parameter Optimization},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year      = {2011}
}

@article{bergstra2012random,
  author  = {Bergstra, James and Bengio, Yoshua},
  title   = {Random Search for Hyper-Parameter Optimization},
  journal = {Journal of Machine Learning Research},
  volume  = {13},
  pages   = {281--305},
  year    = {2012}
}

@inproceedings{golovin2017vizier,
  author    = {Golovin, Daniel and Solnik, Benjamin and Moitra, Subhodeep and
               Kochanski, Greg and Karro, John and Sculley, D.},
  title     = {Google Vizier: A Service for Black-Box Optimization},
  booktitle = {KDD},
  year      = {2017}
}

@article{hansen2001cmaes,
  author  = {Hansen, Nikolaus and Ostermeier, Andreas},
  title   = {Completely Derandomized Self-Adaptation in Evolution Strategies},
  journal = {Evolutionary Computation},
  volume  = {9},
  number  = {2},
  pages   = {159--195},
  year    = {2001}
}

@inproceedings{jamieson2016nonstochastic,
  author    = {Jamieson, Kevin and Talwalkar, Ameet},
  title     = {Non-stochastic Best Arm Identification and Hyperparameter
               Optimization},
  booktitle = {AISTATS},
  year      = {2016}
}

@article{joe2003remark,
  author  = {Joe, Stephen and Kuo, Frances Y.},
  title   = {Remark on Algorithm 659: Implementing Sobol's Quasirandom
             Sequence Generator},
  journal = {ACM Transactions on Mathematical Software},
  volume  = {29},
  number  = {1},
  pages   = {49--57},
  year    = {2003}
}

@article{joe2008sobol,
  author  = {Joe, Stephen and Kuo, Frances Y.},
  title   = {Constructing Sobol Sequences with Better Two-Dimensional
             Projections},
  journal = {SIAM Journal on Scientific Computing},
  volume  = {30},
  number  = {5},
  pages   = {2635--2654},
  year    = {2008}
}

@article{jones1998ego,
  author  = {Jones, Donald R. and Schonlau, Matthias and Welch, William J.},
  title   = {Efficient Global Optimization of Expensive Black-Box Functions},
  journal = {Journal of Global Optimization},
  volume  = {13},
  number  = {4},
  pages   = {455--492},
  year    = {1998}
}

@inproceedings{kennedy1997binarypso,
  author    = {Kennedy, James and Eberhart, Russell C.},
  title     = {A discrete binary version of the particle swarm algorithm},
  booktitle = {IEEE International Conference on Systems, Man, and Cybernetics},
  pages     = {4104--4108},
  year      = {1997}
}

@article{leardi1998ga,
  author  = {Leardi, Riccardo and Lupi{\'a}{\~n}ez Gonz{\'a}lez, Amparo},
  title   = {Genetic algorithms applied to feature selection in PLS
             regression: how and when to use them},
  journal = {Chemometrics and Intelligent Laboratory Systems},
  volume  = {41},
  number  = {2},
  pages   = {195--207},
  year    = {1998}
}

@article{li2018hyperband,
  author  = {Li, Lisha and Jamieson, Kevin and DeSalvo, Giulia and
             Rostamizadeh, Afshin and Talwalkar, Ameet},
  title   = {Hyperband: A Novel Bandit-Based Approach to Hyperparameter
             Optimization},
  journal = {Journal of Machine Learning Research},
  volume  = {18},
  pages   = {1--52},
  year    = {2018}
}

@inproceedings{li2020asha,
  author    = {Li, Liam and Jamieson, Kevin and Rostamizadeh, Afshin and
               Gonina, Ekaterina and Ben-tzur, Jonathan and Hardt, Moritz and
               Recht, Benjamin and Talwalkar, Ameet},
  title     = {A System for Massively Parallel Hyperparameter Tuning},
  booktitle = {Proceedings of Machine Learning and Systems (MLSys)},
  year      = {2020}
}

@article{mckay1979lhs,
  author  = {McKay, Michael D. and Beckman, Richard J. and Conover, William J.},
  title   = {A Comparison of Three Methods for Selecting Values of Input
             Variables in the Analysis of Output from a Computer Code},
  journal = {Technometrics},
  volume  = {21},
  number  = {2},
  pages   = {239--245},
  year    = {1979}
}

@book{rasmussen2006gp,
  author    = {Rasmussen, Carl Edward and Williams, Christopher K. I.},
  title     = {Gaussian Processes for Machine Learning},
  publisher = {MIT Press},
  year      = {2006}
}

@article{sobol1967distribution,
  author  = {Sobol', Ilya M.},
  title   = {On the distribution of points in a cube and the approximate
             evaluation of integrals},
  journal = {USSR Computational Mathematics and Mathematical Physics},
  volume  = {7},
  number  = {4},
  pages   = {86--112},
  year    = {1967}
}

% Software citation is preserved verbatim in nirs4all-methods.cff.