Learning to Score

• URL: http://arxiv.org/abs/2504.14302v1
• Date: Sat, 19 Apr 2025 13:53:38 GMT
• Title: Learning to Score
• Authors: Yogev Kriger, Shai Fine,
• Abstract summary: We study a scenario where the target labels are not available but additional related information is at hand.<n>We formulate the problem as an ensemble of three semantic components: representation learning, side information and metric learning.<n>We demonstrate the utility of the suggested scoring system on well-known benchmark data-sets and bio-medical patient records.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Common machine learning settings range from supervised tasks, where accurately labeled data is accessible, through semi-supervised and weakly-supervised tasks, where target labels are scant or noisy, to unsupervised tasks where labels are unobtainable. In this paper we study a scenario where the target labels are not available but additional related information is at hand. This information, referred to as Side Information, is either correlated with the unknown labels or imposes constraints on the feature space. We formulate the problem as an ensemble of three semantic components: representation learning, side information and metric learning. The proposed scoring model is advantageous for multiple use-cases. For example, in the healthcare domain it can be used to create a severity score for diseases where the symptoms are known but the criteria for the disease progression are not well defined. We demonstrate the utility of the suggested scoring system on well-known benchmark data-sets and bio-medical patient records.

Related papers

• Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labels [6.872072177648135]
  We propose a novel localization algorithm that adapts well-established ground truth estimation methods. Our algorithm also shows superior performance during training on the TexBiG dataset.
  arXiv  Detail & Related papers  (2023-09-18T13:08:44Z)
• Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data [70.25049762295193]
  We introduce a novel conditional image generation framework that accepts noisy-labeled and uncurated data during training. We propose soft curriculum learning, which assigns instance-wise weights for adversarial training while assigning new labels for unlabeled data. Our experiments show that our approach outperforms existing semi-supervised and label-noise robust methods in terms of both quantitative and qualitative performance.
  arXiv  Detail & Related papers  (2023-07-17T08:31:59Z)
• Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations [91.67511167969934]
  imprecise label learning (ILL) is a framework for the unification of learning with various imprecise label configurations. We demonstrate that ILL can seamlessly adapt to partial label learning, semi-supervised learning, noisy label learning, and, more importantly, a mixture of these settings.
  arXiv  Detail & Related papers  (2023-05-22T04:50:28Z)
• Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning [1.827510863075184]
  Supervised contrastive learning is a paradigm that learns similar representations to samples sharing the same class labels. We show that it outperforms cross-entropy in classifying respiratory anomalies in two different datasets. This work suggests the potential of using multiple metadata sources in supervised contrastive settings.
  arXiv  Detail & Related papers  (2022-10-27T12:59:00Z)
• Association Graph Learning for Multi-Task Classification with Category Shifts [68.58829338426712]
  We focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously. We learn an association graph to transfer knowledge among tasks for missing classes. Our method consistently performs better than representative baselines.
  arXiv  Detail & Related papers  (2022-10-10T12:37:41Z)
• Label-Assemble: Leveraging Multiple Datasets with Partial Labels [68.46767639240564]
  "Label-Assemble" aims to unleash the full potential of partial labels from an assembly of public datasets. We discovered that learning from negative examples facilitates both computer-aided disease diagnosis and detection.
  arXiv  Detail & Related papers  (2021-09-25T02:48:17Z)
• Labeled Data Generation with Inexact Supervision [33.110134862501546]
  In this paper, we study a novel problem of labeled data generation with inexact supervision. We propose a novel generative framework named as ADDES which can synthesize high-quality labeled data for target classification tasks.
  arXiv  Detail & Related papers  (2021-06-08T22:22:26Z)
• Exploiting Context for Robustness to Label Noise in Active Learning [47.341705184013804]
  We address the problems of how a system can identify which of the queried labels are wrong and how a multi-class active learning system can be adapted to minimize the negative impact of label noise. We construct a graphical representation of the unlabeled data to encode these relationships and obtain new beliefs on the graph when noisy labels are available. This is demonstrated in three different applications: scene classification, activity classification, and document classification.
  arXiv  Detail & Related papers  (2020-10-18T18:59:44Z)
• Weakly-supervised Salient Instance Detection [65.0408760733005]
  We present the first weakly-supervised approach to the salient instance detection problem. We propose a novel weakly-supervised network with three branches: a Saliency Detection Branch leveraging class consistency information to locate candidate objects; a Boundary Detection Branch exploiting class discrepancy information to delineate object boundaries; and a Centroid Detection Branch using subitizing information to detect salient instance centroids.
  arXiv  Detail & Related papers  (2020-09-29T09:47:23Z)
• Learning Image Labels On-the-fly for Training Robust Classification Models [13.669654965671604]
  We show how noisy annotations (e.g., from different algorithm-based labelers) can be utilized together and mutually benefit the learning of classification tasks. A meta-training based label-sampling module is designed to attend the labels that benefit the model learning the most through additional back-propagation processes.
  arXiv  Detail & Related papers  (2020-09-22T05:38:44Z)
• Structured Prediction with Partial Labelling through the Infimum Loss [85.4940853372503]
  The goal of weak supervision is to enable models to learn using only forms of labelling which are cheaper to collect. This is a type of incomplete annotation where, for each datapoint, supervision is cast as a set of labels containing the real one. This paper provides a unified framework based on structured prediction and on the concept of infimum loss to deal with partial labelling.
  arXiv  Detail & Related papers  (2020-03-02T13:59:41Z)
• Exploratory Machine Learning with Unknown Unknowns [60.78953456742171]
  We study a new problem setting in which there are unknown classes in the training data misperceived as other labels. We propose the exploratory machine learning, which examines and investigates training data by actively augmenting the feature space to discover potentially hidden classes.
  arXiv  Detail & Related papers  (2020-02-05T02:06:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.