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Found 8 papers, 1 papers with code
SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detection
Current techniques for Out-of-Distribution (OoD) detection predominantly rely on quantifying predictive uncertainty and incorporating model regularization during the training phase, using either real or synthetic OoD samples.
Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation
This necessitates rethinking cost-sensitive classification in DNNs.
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning
In this paper we propose \texttt{GIFAIR-FL}: a framework that imposes \textbf{G}roup and \textbf{I}ndividual \textbf{FAIR}ness to \textbf{F}ederated \textbf{L}earning settings.
SALR: Sharpness-aware Learning Rate Scheduler for Improved Generalization
In an effort to improve generalization in deep learning and automate the process of learning rate scheduling, we propose SALR: a sharpness-aware learning rate update technique designed to recover flat minimizers.
SALR: Sharpness-aware Learning Rates for Improved Generalization
In an effort to improve generalization in deep learning, we propose SALR: a sharpness-aware learning rate update technique designed to recover flat minimizers.
Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances
The min-max optimization problem, also known as the saddle point problem, is a classical optimization problem which is also studied in the context of zero-sum games.
Rényi Fair Inference
In this paper, we use R\'enyi correlation as a measure of fairness of machine learning models and develop a general training framework to impose fairness.
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods
In this paper, we study the problem in the non-convex regime and show that an \varepsilon--first order stationary point of the game can be computed when one of the player's objective can be optimized to global optimality efficiently.
