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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arXiv Machine Learning
Jul 31
Distributions In, Distributions Out: The Case for Soft-Label Training
arXiv:2511. 14117v2 Announce Type: replace Abstract: Supervised classifiers output a distribution over classes but are typically trained against a single label obtained by collapsing multiple annotators into a majority vote.
arXiv Machine Learning
Jul 9
Optimized Instance Alteration for Explaining and Assessing Robustness of Classifiers
arXiv:2607. 06637v1 Announce Type: new Abstract: In this work, we propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers.
arXiv AI
Jun 11
Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning
arXiv:2602. 08986v2 Announce Type: replace-cross Abstract: In hierarchical multi-label classification, a persistent challenge is enabling model predictions to reach deeper levels of the hierarchy for more detailed or fine-grained classifications.
arXiv Machine Learning
Aug 4
Benign Overfitting in Linear Classifiers with a Bias Term
arXiv:2511. 12840v2 Announce Type: replace-cross Abstract: Overparameterized models often generalize well even when they interpolate noisy training data.
arXiv AI
Jul 15
Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite
arXiv:2607. 11947v1 Announce Type: cross Abstract: Typical semi-supervised learning (SSL) methods rely on distributional assumptions, and their performance degrades when these are violated.
arXiv Machine Learning
Jul 14
Error Aware Distribution Prediction for Lightweight Implicit Neural Representations
arXiv:2607. 10068v1 Announce Type: new Abstract: Implicit neural representations (INRs) offer compact encoding of volumes, but as lossy approximators, inevitably have prediction errors.
arXiv Machine Learning
Jul 8
Universality of Benign Overfitting in Binary Linear Classification
arXiv:2501. 10538v3 Announce Type: replace Abstract: The practical success of deep learning has led to the discovery of several surprising phenomena.
arXiv Machine Learning
Jul 16
Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels
arXiv:2604. 06614v2 Announce Type: replace-cross Abstract: Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language models to downstream tasks.
arXiv Machine Learning
Jun 9
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
arXiv:2403. 06013v2 Announce Type: replace Abstract: This paper delves into the critical area of deep learning robustness, challenging the conventional belief that classification robustness and explanation robustness in image classification systems are inherently correlated.
arXiv AI
Jun 11
CoVar: Confidence-Variance-Guided Pseudo-Label Selection for Semi-Supervised Learning
arXiv:2601. 11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance.
arXiv Machine Learning
Jun 9
Similarity-Distance-Magnitude Activations
arXiv:2509. 12760v5 Announce Type: replace Abstract: We introduce the Similarity-Distance-Magnitude (SDM) activation function, a more robust and interpretable formulation of the standard softmax activation function, adding Similarity (i.