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Fair AutoML Through Multi-objective Optimization
There has been a recent surge of interest in fairness measurement and bias mitigation in machine learning, given the identification of significant disparities in predictions from models in many domains.
R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes
While self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches, violations of the static world assumption can still lead to erroneous depth predictions of traffic participants, posing a potential safety issue.
Constrained Multi-Objective Optimization for Automated Machine Learning
In this work, we present a framework called Autotune that effectively handles multiple objectives and constraints that arise in machine learning problems.
Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning
For hyperparameter tuning, machine learning algorithms are complex black-boxes.
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