Hyperparameter Optimization Is Deceiving Us, and How to Stop It

5 February 2021

Papers citing "Hyperparameter Optimization Is Deceiving Us, and How to Stop It"

8 / 8 papers shown

Title
The Curious Case of Arbitrariness in Machine Learning Prakhar Ganesh Afaf Taik G. Farnadi 59 2 0 28 Jan 2025
Learning from Uncertain Data: From Possible Worlds to Possible Models Jiongli Zhu Su Feng Boris Glavic Babak Salimi 22 0 0 28 May 2024
Predictive Churn with the Set of Good Models J. Watson-Daniels Flavio du Pin Calmon Alexander DÁmour Carol Xuan Long David C. Parkes Berk Ustun 79 7 0 12 Feb 2024
Adversarial Scrutiny of Evidentiary Statistical Software Rediet Abebe Moritz Hardt Angela Jin John Miller Ludwig Schmidt Rebecca Wexler 28 5 0 19 Jun 2022
deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks Dennis Ulmer Christian Hardmeier J. Frellsen 40 42 0 14 Apr 2022
Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning A. Feder Cooper Emanuel Moss Benjamin Laufer Helen Nissenbaum MLAU 24 85 0 10 Feb 2022
Model Selection's Disparate Impact in Real-World Deep Learning Applications Jessica Zosa Forde A. Feder Cooper Kweku Kwegyir-Aggrey Chris De Sa Michael Littman 11 22 0 01 Apr 2021
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 264 5,326 0 05 Nov 2016