Can you rely on your model evaluation? improving model evaluation with synthetic test data

B van Breugel, N Seedat, F Imrie… - Advances in Neural …, 2024 - proceedings.neurips.cc
Evaluating the performance of machine learning models on diverse and underrepresented
subgroups is essential for ensuring fairness and reliability in real-world applications …
被引用次数：6 相关文章

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

B van Breugel, N Seedat, F Imrie… - arXiv e …, 2023 - ui.adsabs.harvard.edu
Evaluating the performance of machine learning models on diverse and underrepresented
subgroups is essential for ensuring fairness and reliability in real-world applications …

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

B van Breugel, N Seedat, F Imrie… - arXiv preprint arXiv …, 2023 - arxiv.org
Evaluating the performance of machine learning models on diverse and underrepresented
subgroups is essential for ensuring fairness and reliability in real-world applications …

Can you rely on your model evaluation? improving model evaluation with synthetic test data

B van Breugel, N Seedat, F Imrie… - Proceedings of the 37th …, 2023 - dl.acm.org
Evaluating the performance of machine learning models on diverse and underrepresented
subgroups is essential for ensuring fairness and reliability in real-world applications …

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

B van Breugel, N Seedat, F Imrie… - … -seventh Conference on … - openreview.net
Evaluating the performance of machine learning models on diverse and underrepresented
subgroups is essential for ensuring fairness and reliability in real-world applications …