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Found 10 papers, 7 papers with code
Revisiting AUC-oriented Adversarial Training with Loss-Agnostic Perturbations
On top of this, we can show that: 1) Under mild conditions, AdAUC can be optimized equivalently with score-based or instance-wise-loss-based perturbations, which is compatible with most of the popular adversarial example generation methods.
AUC-Oriented Domain Adaptation: From Theory to Algorithm
We propose a new result that not only addresses the interdependency issue but also brings a much sharper bound with weaker assumptions about the loss function.
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm
sample size and a slow convergence rate, especially for TPAUC.
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering.
AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems
Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem.
Optimizing Two-way Partial AUC with an End-to-end Framework
The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss.
Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling
However, in this work, by taking a theoretical analysis, we find that negative sampling would lead to a biased estimation of the generalization error.
Learning with Multiclass AUC: Theory and Algorithms
Our foundation is based on the M metric, which is a well-known multiclass extension of AUC.
When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC.
The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss.
Collaborative Preference Embedding against Sparse Labels
From the margin theory point-of-view, we then propose a generalization enhancement scheme for sparse and insufficient labels via optimizing the margin distribution.
