Fetching the paper…
Reading the bibliography…
In practice, machine learning (ML) workflows require various different steps, from data preprocessing, missing value imputation, model selection, to model tuning as well as model evaluation.
Varying-coefficient models
Hastie, T. and R. Tibshirani (1993) · 1993
Earlier work this paper cites.
Experiments with a new boosting algorithm
Freund, Y., R. E. Schapire, et al. (1996) · 1996
Earlier work this paper cites.
Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust autodl
Zimmer, L., M. Lindauer, and F. Hutter (2021) · 1996
Earlier work this paper cites.
Random forests
Breiman, L. (2001) · 2001
Earlier work this paper cites.
Drozdal, J., J. D. Weisz, D. Wang, G. Dass, B. Yao, C. Zhao, M. J. Muller, L. Ju, and H. Su (2020) · 2001
Earlier work this paper cites.
Boosting with the L2 loss: regression and classification
Bühlmann, P. and B. Yu (2003) · 2003
Earlier work this paper cites.
Discussion: A tale of three cousins: Lasso, L2Boosting and Dantzig
Meinshausen, N., G. Rocha, and B. Yu (2007) · 2007
Earlier work this paper cites.
A framework for unbiased model selection based on boosting
Hofner, B., T. Hothorn, T. Kneib, and M. Schmid (2011) · 2011
Earlier work this paper cites.
Gene expression profiling predicts the development of oral cancer
Saintigny, P., L. Zhang, Y.-H. Fan, A. K. El-Naggar, V. A. Papadimitrakopoulou, L. Feng, J. J. Lee, E. S. Kim, W. K. Hong, and L. Mao (2011) · 2011
Earlier work this paper cites.
gamboostLSS: An R package for model building and variable selection in the GAMLSS framework
Hofner, B., A. Mayr, and M. Schmid (2016) · 2016
Earlier work this paper cites.
Non-stochastic best arm identification and hyperparameter optimization
Jamieson, K. and A. Talwalkar (2016) · 2016
Earlier work this paper cites.
batchtools: Tools for r to work on batch systems
Lang, M., B. Bischl, and D. Surmann (2017, feb) · 2017
Cited alongside, same era.
Probing for sparse and fast variable selection with model-based boosting
Thomas, J., T. Hepp, A. Mayr, and B. Bischl (2017) · 2017
Cited alongside, same era.
Generalized additive models: an introduction with r
Wood, S. N. (2017) · 2017
Cited alongside, same era.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L., K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar (2018) · 2018
Cited alongside, same era.
Boosting factor-specific functional historical models for the detection of synchronization in bioelectrical signals
Rügamer, D., S. Brockhaus, K. Gentsch, K. Scherer, and S. Greven (2018) · 2018
Cited alongside, same era.
compboost: Modular Framework for Component-wise Boosting
Schalk, D., J. Thomas, and B. Bischl (2018) · 2018
mlr3: A modern object-oriented machine learning framework in R
Lang, M., M. Binder, J. Richter, P. Schratz, F. Pfisterer, S. Coors, Q. Au, G. Casalicchio, L. Kotthoff, and B. Bischl (2019, dec) · 2019
Later among the works it cites.
Towards human centered automl
Pfisterer, F., J. Thomas, and B. Bischl (2019) · 2019
Later among the works it cites.
Boosting functional regression models with fdboost
Brockhaus, S., D. Rügamer, and S. Greven (2020) · 2020
Later among the works it cites.
Autogluon-tabular: Robust and accurate automl for structured data
Erickson, N., J. Mueller, A. Shirkov, H. Zhang, P. Larroy, M. Li, and A. Smola (2020) · 2020
Later among the works it cites.
Classifying neck pain status using scalar and functional biomechanical variables – Development of a method using functional data boosting
Liew, B. X., D. Rugamer, A. Stocker, and A. M. De Nunzio (2020) · 2020
Later among the works it cites.
Inference for L2-Boosting
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Automatic gradient boosting
Thomas, J., S. Coors, and B. Bischl (2018) · 2018
Cited alongside, same era.
Auto-sklearn: efficient and robust automated machine learning
Feurer, M., A. Klein, K. Eggensperger, J. T. Springenberg, M. Blum, and F. Hutter (2019) · 2019
Cited alongside, same era.
Automated machine learning for studying the trade-off between predictive accuracy and interpretability
Freitas, A. A. (2019, August) · 2019
Cited alongside, same era.
An open source automl benchmark
Gijsbers, P., E. LeDell, S. Poirier, J. Thomas, B. Bischl, and J. Vanschoren (2019) · 2019
Cited alongside, same era.
Auto-weka: Automatic model selection and hyperparameter optimization in weka
Kotthoff, L., C. Thornton, H. H. Hoos, F. Hutter, and K. Leyton-Brown (2019) · 2019
Cited alongside, same era.
Flexible smoothing with B-splines and penalties
Eilers, P. H. and B. D. Marx (1996a)
Cited in the paper.
Rügamer, D. and S. Greven (2020) · 2020
Later among the works it cites.
Putting the human back in the automl loop
Xanthopoulos, I., I. Tsamardinos, V. Christophides, E. Simon, and A. Salinger (2020) · 2020
Later among the works it cites.
mlr3pipelines - flexible machine learning pipelines in r
Binder, M., F. Pfisterer, M. Lang, L. Schneider, L. Kotthoff, and B. Bischl (2021) · 2021
Closest in time.
R: A Language and Environment for Statistical Computing
R Core Team (2021) · 2021
Closest in time.
Semi-structured deep distributional regression: Combining structured additive models and deep learning
Rügamer, D., C. Kolb, and N. Klein (2021) · 2021
Closest in time.