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Approximate Cross-validation: Guarantees for Model Assessment and
  Selection
v1v2 (latest)

Approximate Cross-validation: Guarantees for Model Assessment and Selection

International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
2 March 2020
Ashia Wilson
Maximilian Kasy
Lester W. Mackey
ArXiv (abs)PDFHTML

Papers citing "Approximate Cross-validation: Guarantees for Model Assessment and Selection"

14 / 14 papers shown
Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings
Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings
Jenny Y. Huang
Yunyi Shen
Dennis Wei
Tamara Broderick
ALM
240
2
0
16 Aug 2025
Failures and Successes of Cross-Validation for Early-Stopped Gradient
  Descent
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent
Pratik Patil
Yuchen Wu
Robert Tibshirani
284
8
0
26 Feb 2024
Inconsistency of cross-validation for structure learning in Gaussian
  graphical models
Inconsistency of cross-validation for structure learning in Gaussian graphical models
Zhao Lyu
Wai Ming Tai
Mladen Kolar
Bryon Aragam
CML
195
0
0
28 Dec 2023
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
G. Iyengar
Henry Lam
Jiashuo Liu
568
6
0
16 Jun 2023
Iterative Approximate Cross-Validation
Iterative Approximate Cross-ValidationInternational Conference on Machine Learning (ICML), 2023
Yuetian Luo
Zhimei Ren
Rina Foygel Barber
288
8
0
05 Mar 2023
Extrapolated cross-validation for randomized ensembles
Extrapolated cross-validation for randomized ensemblesJournal of Computational And Graphical Statistics (JCGS), 2023
Jin-Hong Du
Pratik V. Patil
Kathryn Roeder
Arun K. Kuchibhotla
524
9
0
27 Feb 2023
Algorithms that Approximate Data Removal: New Results and Limitations
Algorithms that Approximate Data Removal: New Results and LimitationsNeural Information Processing Systems (NeurIPS), 2022
Vinith Suriyakumar
Ashia Wilson
MU
247
48
0
25 Sep 2022
Can we globally optimize cross-validation loss? Quasiconvexity in ridge
  regression
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regressionNeural Information Processing Systems (NeurIPS), 2021
William T. Stephenson
Zachary Frangella
Madeleine Udell
Tamara Broderick
325
15
0
19 Jul 2021
Synthetic Data for Model Selection
Synthetic Data for Model SelectionInternational Conference on Machine Learning (ICML), 2021
Alon Shoshan
Nadav Bhonker
Igor Kviatkovsky
Matan Fintz
Gérard Medioni
218
7
0
03 May 2021
Leave Zero Out: Towards a No-Cross-Validation Approach for Model
  Selection
Leave Zero Out: Towards a No-Cross-Validation Approach for Model Selection
Weikai Li
Chuanxing Geng
Songcan Chen
387
15
0
24 Dec 2020
Approximate Cross-Validation with Low-Rank Data in High Dimensions
Approximate Cross-Validation with Low-Rank Data in High Dimensions
William T. Stephenson
Madeleine Udell
Tamara Broderick
265
2
0
24 Aug 2020
Stochastic Optimization Forests
Stochastic Optimization Forests
Nathan Kallus
Xiaojie Mao
497
62
0
17 Aug 2020
Cross-validation Confidence Intervals for Test Error
Cross-validation Confidence Intervals for Test ErrorNeural Information Processing Systems (NeurIPS), 2020
Pierre Bayle
Alexandre Bayle
Lucas Janson
Lester W. Mackey
339
61
0
24 Jul 2020
Approximate Cross-Validation for Structured Models
Approximate Cross-Validation for Structured ModelsNeural Information Processing Systems (NeurIPS), 2020
S. Ghosh
William T. Stephenson
Tin D. Nguyen
Sameer K. Deshpande
Tamara Broderick
250
18
0
23 Jun 2020
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