When to retrain a machine learning model

20 May 2025

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Papers citing "When to retrain a machine learning model"

26 / 26 papers shown

Title
Model-Based Uncertainty in Value Functions Carlos E. Luis A. Bottero Julia Vinogradska Felix Berkenkamp Jan Peters 73 15 0 24 Feb 2023
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time Huaxiu Yao Caroline Choi Bochuan Cao Yoonho Lee Pang Wei Koh Chelsea Finn OOD 68 76 0 25 Nov 2022
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification Leo Schwinn Leon Bungert A. Nguyen René Raab Falk Pulsmeyer Doina Precup Björn Eskofier Dario Zanca OOD 76 15 0 19 May 2022
Training Compute-Optimal Large Language Models Jordan Hoffmann Sebastian Borgeaud A. Mensch Elena Buchatskaya Trevor Cai ... Karen Simonyan Erich Elsen Jack W. Rae Oriol Vinyals Laurent Sifre AI4TS 203 1,949 0 29 Mar 2022
Offline Reinforcement Learning with Implicit Q-Learning Ilya Kostrikov Ashvin Nair Sergey Levine OffRL 288 910 0 12 Oct 2021
Predicting with Confidence on Unseen Distributions Devin Guillory Vaishaal Shankar Sayna Ebrahimi Trevor Darrell Ludwig Schmidt UQCV OOD 57 122 0 07 Jul 2021
Offline Reinforcement Learning as One Big Sequence Modeling Problem Michael Janner Qiyang Li Sergey Levine OffRL 142 684 0 03 Jun 2021
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning Aodong Li Alex Boyd Padhraic Smyth Stephan Mandt 44 24 0 15 Dec 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai ... Matthias Minderer G. Heigold Sylvain Gelly Jakob Uszkoreit N. Houlsby ViT 657 41,103 0 22 Oct 2020
Energy-based Out-of-distribution Detection Weitang Liu Xiaoyun Wang John Douglas Owens Yixuan Li OODD 271 1,356 0 08 Oct 2020
Reactive Soft Prototype Computing for Concept Drift Streams Christoph Raab Moritz Heusinger Frank-Michael Schleif 32 121 0 10 Jul 2020
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? Angelos Filos P. Tigas R. McAllister Nicholas Rhinehart Sergey Levine Y. Gal 63 187 0 26 Jun 2020
Conservative Q-Learning for Offline Reinforcement Learning Aviral Kumar Aurick Zhou George Tucker Sergey Levine OffRL OnRL 140 1,824 0 08 Jun 2020
Rethinking Importance Weighting for Deep Learning under Distribution Shift Tongtong Fang Nan Lu Gang Niu Masashi Sugiyama 62 139 0 08 Jun 2020
The iWildCam 2020 Competition Dataset Sara Beery Elijah Cole Arvi Gjoka 125 90 0 21 Apr 2020
A Unified View of Label Shift Estimation Saurabh Garg Yifan Wu Sivaraman Balakrishnan Zachary Chase Lipton 69 144 0 17 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 605 4,822 0 23 Jan 2020
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift Stephan Rabanser Stephan Günnemann Zachary Chase Lipton 59 370 0 29 Oct 2018
Scikit-Multiflow: A Multi-output Streaming Framework Jacob Montiel Jesse Read Albert Bifet T. Abdessalem 35 308 0 12 Jul 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 143 790 0 30 Apr 2018
Online Learning: A Comprehensive Survey Guosheng Lin Doyen Sahoo Jing Lu P. Zhao OffRL 70 644 0 08 Feb 2018
The Uncertainty Bellman Equation and Exploration Brendan O'Donoghue Ian Osband Rémi Munos Volodymyr Mnih 68 192 0 15 Sep 2017
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks Dan Hendrycks Kevin Gimpel UQCV 158 3,454 0 07 Oct 2016
XGBoost: A Scalable Tree Boosting System Tianqi Chen Carlos Guestrin 809 38,961 0 09 Mar 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,020 0 10 Dec 2015
An Introduction to Convolutional Neural Networks K. O’Shea Ryan Nash FaML HAI 76 3,152 0 26 Nov 2015