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.
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.
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arXiv Machine Learning
Jul 27
The Role of Pseudo-labels in Self-training Linear Classifiers on High-dimensional Gaussian Mixture Data
arXiv:2205. 07739v4 Announce Type: replace-cross Abstract: Self-training (ST) is a simple yet effective semi-supervised learning method.
arXiv Machine Learning
Aug 7
Minimax Optimal Early-Stopped Gradient Descent for Gaussian Mixture Classification
arXiv:2608. 06250v1 Announce Type: cross Abstract: In overparameterised classification, training data can be linearly separable even when the underlying distribution is not.
arXiv Machine Learning
Jun 19
Benign overfitting beyond prediction: The ordinary least squares interpolator
arXiv:2309. 15769v3 Announce Type: replace-cross Abstract: Recent advances in deep learning have highlighted the phenomenon of benign overfitting in overparameterized statistical models, sparking significant interest in understanding its foundations.
arXiv Machine Learning
Aug 11
Efficient and Noise-Tolerant PAC Learning of Multiclass Linear Classifiers
arXiv:2605. 18662v2 Announce Type: replace Abstract: Noise-tolerant PAC learning of linear models has been of central interests in machine learning community since the last century.
Hugging Face Trending Papers
Jun 11
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.
arXiv Machine Learning
Jul 21
AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labels
arXiv:2605. 28021v2 Announce Type: replace Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs may deviate from the training distribution and overconfident predictions on unknown samples can lead to unreliable decisions.
arXiv Machine Learning
Jul 7
Improved generalization bounds for binary linear classification via isoperimetry
arXiv:2505. 16713v3 Announce Type: replace-cross Abstract: We examine the concentration of uniform generalization errors around their expectation in binary linear classification problems via an isoperimetric argument.
arXiv AI
Aug 6
LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling
arXiv:2608. 04147v1 Announce Type: cross Abstract: Label noise is common in medical imaging datasets due to factors such as inter-rater variability, annotation errors, and ambiguous cases.
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
Jul 30
The Advantage of Fine-Grained Training
arXiv:2509. 05130v2 Announce Type: replace Abstract: In classification problems, models are trained to predict a class label based on the input data features.
arXiv Machine Learning
Jun 8
Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization
arXiv:2505. 21423v3 Announce Type: replace Abstract: The remarkable generalization properties of overparameterized networks are often attributed to implicit biases, such as norm minimization at small learning rates and low sharpness in the Edge-of-Stability regime.