mlsanityData to 7 Oct 2026

Comments on Leo Breiman's paper 'Statistical Modeling: The Two Cultures' (Statistical Science, 2001, 16(3), 199-231)

arXiv 2103.11327 ↗First posted 21 Mar 2021stat.ML stat.MEcited by 0

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Abstract (arXiv)

Breiman challenged statisticians to think more broadly, to step into the unknown, model-free learning world, with him paving the way forward. Statistics community responded with slight optimism, some skepticism, and plenty of disbelief. Today, we are at the same crossroad anew. Faced with the enormous practical success of model-free, deep, and machine learning, we are naturally inclined to think that everything is resolved. A new frontier has emerged; the one where the role, impact, or stability of the it learning algorithms is no longer measured by prediction quality, but an inferential one -- asking the questions of it why and it if can no longer be safely ignored.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 54 references that are in the map. First posted on arXiv 21 Mar 2021.

Builds on

24 of 54 references.

References · 24

  1. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
  2. 2018Reconciling modern machine learning practice and the bias-variance trade-offcited by 919
  3. 2019Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descentcited by 732
  4. 2016Distribution-Free Predictive Inference For Regressioncited by 657
  5. 2016Generalized Random Forestscited by 309
  6. 2019Predictive inference with the jackknife+cited by 206
  7. 2019Two models of double descent for weak featurescited by 194
  8. 2018To understand deep learning we need to understand kernel learningcited by 186
  9. 2017The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learningcited by 150
  10. 2013Stabilitycited by 119
  11. 2020Implicit Regularization in Deep Learning May Not Be Explainable by Normscited by 118
  12. 2014Controlling the false discovery rate via knockoffscited by 97
12 more
  1. 2019Harnessing the Power of Infinitely Wide Deep Nets on Small-data Taskscited by 87
  2. 2014Consistency of random forestscited by 84
  3. 2019Veridical Data Sciencecited by 65
  4. 2019Universal Inferencecited by 43
  5. 2017Geometry of Optimization and Implicit Regularization in Deep Learningcited by 39
  6. 2016Semi-supervised Inference: General Theory and Estimation of Meanscited by 38
  7. 2020On the infinite width limit of neural networks with a standard parameterizationcited by 38
  8. 2017Local nearest neighbour classification with applications to semi-supervised learningcited by 17
  9. 2019Sparsity Double Robust Inference of Average Treatment Effectscited by 7
  10. 2019Minimax Semiparametric Learning With Approximate Sparsitycited by 6
  11. 2018Testability of high-dimensional linear models with non-sparse structurescited by 4
  12. 2016Significance testing in non-sparse high-dimensional linear modelscited by 3

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