arXiv Machine Learning
Jul 21
Robust Losses from Univariate Base Functions for Noisy-Label Learning
arXiv:2607. 16768v1 Announce Type: new Abstract: Learning with noisy labels is a fundamental problem in training reliable deep neural networks.
Distributional Loss for Robust Classification
This paper proposes a novel loss concept for supervised classification tasks. Rather than enforcing a direct mapping from each input sample to a single assigned label, we define an optimization objective over all classifier outputs as a bimodal Gaussian distribution.
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Optimized Instance Alteration for Explaining and Assessing Robustness of Classifiers
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Benign Overfitting in Linear Classifiers with a Bias Term
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Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite
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arXiv Machine Learning
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General Quantification of Covariate and Concept Shifts
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arXiv Machine Learning
Jul 14
Error Aware Distribution Prediction for Lightweight Implicit Neural Representations
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Universality of Benign Overfitting in Binary Linear Classification
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Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels
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arXiv Machine Learning
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Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
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