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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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6 results for “label-wise”

Label-wise Aleatoric and Epistemic Uncertainty Quantificationcs.LG

New method quantifies uncertainty at class level for better decision-making.

problem Improving cost-sensitive decision-making in classification tasks.

method Label-wise decomposition of uncertainty measures based on non-categorical metrics.

result Proposed measures adhere to desirable properties and improve uncertainty quantification.

uncertainty quantification classification tasks cost-sensitive decision-making label-wise non-categorical measures

2024-06-04abs ↗pdf ↗

Towards Understanding Generalization of Macro-AUC in Multi-label Learningcs.LG

The study examines the generalization of Macro-AUC in multi-label learning, identifying label imbalance as a critical factor.

problem Theoretical understanding of Macro-AUC in multi-label learning is lacking.

method Characterization of generalization properties of learning algorithms based on surrogate losses w.r.t. Macro-AUC, identification of label imbalance as a critical factor.

result The widely-used univariate loss-based algorithm is more sensitive to label imbalance than pairwise and reweighted loss-based ones, implying worse performance.

Macro-AUC multi-label learning label imbalance surrogate losses generalization bounds

2023-05-09abs ↗pdf ↗

Collaborative Graph Walk for Semi-supervised Multi-Label Node Classificationcs.LG

In this work, we study semi-supervised multi-label node classification problem in attributed graphs. Classic solutions to multi-label node classification follow two steps, first learn node embedding and then build a node classifier on the learned embedding. To improve the discriminating power of the node embedding, we …

2019-10-22abs ↗pdf ↗

Learning Gradient Boosted Multi-label Classification Rulescs.LG

Develops gradient boosting for multi-label classification.

problem Lack of customizable learning algorithms for multi-label classification.

method Generalizes gradient boosting to multi-output problems and proposes an algorithm for learning multi-label classification rules.

result Ability to minimize both decomposable and non-decomposable loss functions.

gradient boosting multi-label classification loss functions Hamming loss subset 0/1 loss

2020-06-23abs ↗pdf ↗

A Variational Dirichlet Framework for Out-of-Distribution Detectioncs.LG

With the recently rapid development in deep learning, deep neural networks have been widely adopted in many real-life applications. However, deep neural networks are also known to have very little control over its uncertainty for unseen examples, which potentially causes very harmful and annoying consequences in practi…

2018-11-18abs ↗pdf ↗

Ensemble Multi-Source Domain Adaptation with Pseudolabelscs.LG

EnMDAP aligns conditional distributions for multi-source domain adaptation using pseudolabels.

problem Training a target model with no labeled data in the absence of target data labels.

method EnMDAP uses label-wise moment matching and ensemble learning with multiple feature extractors.

result EnMDAP achieves state-of-the-art performance in multi-source domain adaptation tasks.

domain adaptation ensemble learning pseudolabels conditional distributions

2020-09-29abs ↗pdf ↗