The Shape of Learning Curves: a Review

19 March 2021

Papers citing "The Shape of Learning Curves: a Review"

40 / 40 papers shown

Title
Monotone Learning Olivier Bousquet Amit Daniely Haim Kaplan Yishay Mansour Shay Moran Uri Stemmer 16 10 0 10 Feb 2022
Learning Curves for Decision Making in Supervised Machine Learning: A Survey F. Mohr Jan N. van Rijn 68 55 0 28 Jan 2022
Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets Alethea Power Yuri Burda Harrison Edwards Igor Babuschkin Vedant Misra 73 354 0 06 Jan 2022
Fast and Informative Model Selection using Learning Curve Cross-Validation F. Mohr Jan N. van Rijn 42 32 0 27 Nov 2021
Scaling Vision Transformers Xiaohua Zhai Alexander Kolesnikov N. Houlsby Lucas Beyer ViT 128 1,080 0 08 Jun 2021
Learning Curve Theory Marcus Hutter 188 62 0 08 Feb 2021
Risk-Monotonicity in Statistical Learning Zakaria Mhammedi 68 8 0 28 Nov 2020
A Theory of Universal Learning Olivier Bousquet Steve Hanneke Shay Moran Ramon van Handel Amir Yehudayoff 57 54 0 09 Nov 2020
Learning Curves for Analysis of Deep Networks Derek Hoiem Tanmay Gupta Zhizhong Li Michal Shlapentokh-Rothman 49 26 0 21 Oct 2020
A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning A. Mey 25 3 0 06 Oct 2020
Small Data, Big Decisions: Model Selection in the Small-Data Regime J. Bornschein Francesco Visin Simon Osindero 31 39 0 26 Sep 2020
A critical analysis of metrics used for measuring progress in artificial intelligence Kathrin Blagec Georg Dorffner M. Moradi Matthias Samwald 55 33 0 06 Aug 2020
Multiple Descent: Design Your Own Generalization Curve Lin Chen Yifei Min M. Belkin Amin Karbasi DRL 67 61 0 03 Aug 2020
Triple descent and the two kinds of overfitting: Where & why do they appear? Stéphane dÁscoli Levent Sagun Giulio Biroli 39 80 0 05 Jun 2020
A Brief Prehistory of Double Descent Marco Loog T. Viering A. Mey Jesse H. Krijthe David Tax 43 69 0 07 Apr 2020
Optimal Regularization Can Mitigate Double Descent Preetum Nakkiran Prayaag Venkat Sham Kakade Tengyu Ma 78 132 0 04 Mar 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 522 4,773 0 23 Jan 2020
More Data Can Hurt for Linear Regression: Sample-wise Double Descent Preetum Nakkiran 42 68 0 16 Dec 2019
Deep Double Descent: Where Bigger Models and More Data Hurt Preetum Nakkiran Gal Kaplun Yamini Bansal Tristan Yang Boaz Barak Ilya Sutskever 119 935 0 04 Dec 2019
Making Learners (More) Monotone T. Viering A. Mey Marco Loog 19 10 0 25 Nov 2019
A Constructive Prediction of the Generalization Error Across Scales Jonathan S. Rosenfeld Amir Rosenfeld Yonatan Belinkov Nir Shavit 91 211 0 27 Sep 2019
Minimizers of the Empirical Risk and Risk Monotonicity Marco Loog T. Viering A. Mey 42 28 0 11 Jul 2019
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves Armin Lederer Jonas Umlauft Sandra Hirche 47 25 0 04 Jun 2019
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size! Niels Bruun Ipsen Lars Kai Hansen 28 6 0 02 May 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 155 743 0 19 Mar 2019
A review of domain adaptation without target labels Wouter M. Kouw Marco Loog OOD VLM 34 486 0 16 Jan 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 195 1,638 0 28 Dec 2018
Characterizing and Avoiding Negative Transfer Zirui Wang Zihang Dai Barnabás Póczós J. Carbonell 85 415 0 24 Nov 2018
A jamming transition from under- to over-parametrization affects loss landscape and generalization S. Spigler Mario Geiger Stéphane dÁscoli Levent Sagun Giulio Biroli Matthieu Wyart 58 153 0 22 Oct 2018
Exploring the Limits of Weakly Supervised Pretraining D. Mahajan Ross B. Girshick Vignesh Ramanathan Kaiming He Manohar Paluri Yixuan Li Ashwin R. Bharambe Laurens van der Maaten VLM 176 1,367 0 02 May 2018
Deep Learning Scaling is Predictable, Empirically Joel Hestness Sharan Narang Newsha Ardalani G. Diamos Heewoo Jun Hassan Kianinejad Md. Mostofa Ali Patwary Yang Yang Yanqi Zhou 87 736 0 01 Dec 2017
Supervised Classification: Quite a Brief Overview Marco Loog SSL 34 21 0 25 Oct 2017
High-dimensional dynamics of generalization error in neural networks Madhu S. Advani Andrew M. Saxe AI4CE 128 469 0 10 Oct 2017
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era Chen Sun Abhinav Shrivastava Saurabh Singh Abhinav Gupta VLM 159 2,393 0 10 Jul 2017
The Peaking Phenomenon in Semi-supervised Learning Jesse H. Krijthe Marco Loog SSL 31 6 0 17 Oct 2016
Learning Visual Features from Large Weakly Supervised Data Armand Joulin Laurens van der Maaten Allan Jabri Nicolas Vasilache SSL 87 406 0 06 Nov 2015
Introducing Geometry in Active Learning for Image Segmentation Ksenia Konyushkova Raphael Sznitman Pascal Fua 38 75 0 20 Aug 2015
Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It Peter Grünwald T. V. Ommen 77 267 0 11 Dec 2014
When Ignorance is Bliss Peter Grünwald Joseph Y. Halpern 44 78 0 27 Jul 2014
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss Shai Ben-David D. Loker Nathan Srebro Karthik Sridharan 76 65 0 27 Jun 2012