Can You Rely on Your Model Evaluation? Improving Model Evaluation with
Synthetic Test Data

Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data

25 October 2023

Papers citing "Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data"

9 / 9 papers shown

Title
Generative Conditional Distributions by Neural (Entropic) Optimal Transport Bao Nguyen Binh Nguyen Hieu Trung Nguyen Viet Anh Nguyen OT 31 1 0 04 Jun 2024
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets Ossi Raisa Antti Honkela 67 0 0 06 Feb 2024
A structured regression approach for evaluating model performance across intersectional subgroups Christine Herlihy Kimberly Truong Alexandra Chouldechova Miroslav Dudik 19 4 0 26 Jan 2024
Synthetic data, real errors: how (not) to publish and use synthetic data B. V. Breugel Zhaozhi Qian M. Schaar SyDa 57 28 0 16 May 2023
A Fine-Grained Analysis on Distribution Shift Olivia Wiles Sven Gowal Florian Stimberg Sylvestre-Alvise Rebuffi Ira Ktena Krishnamurthy Dvijotham A. Cemgil OOD 215 196 0 21 Oct 2021
Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data Hari Prasanna Das Ryan Tran Japjot Singh Xiangyu Yue G. Tison Alberto L. Sangiovanni-Vincentelli C. Spanos OOD MedIm 50 51 0 14 Sep 2021
Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance Andrew C. Miller Leon A. Gatys Joseph D. Futoma E. Fox 17 8 0 25 Apr 2021
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models Ahmed Alaa B. V. Breugel Evgeny S. Saveliev M. Schaar 38 186 0 17 Feb 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016