mlsanityData to 7 Oct 2026

Learning under Temporal Label Noise

arXiv 2402.04398 ↗First posted 6 Feb 2024cs.LG cs.AI stat.MLcited by 4

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Abstract (arXiv)

Many time series classification tasks, where labels vary over time, are affected by label noise that also varies over time. Such noise can cause label quality to improve, worsen, or periodically change over time. We first propose and formalize temporal label noise, an unstudied problem for sequential classification of time series. In this setting, multiple labels are recorded over time while being corrupted by a time-dependent noise function. We first demonstrate the importance of modeling the temporal nature of the label noise function and how existing methods will consistently underperform. We then propose methods to train noise-tolerant classifiers by estimating the temporal label noise function directly from data. We show that our methods lead to state-of-the-art performance under diverse types of temporal label noise on real-world datasets

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 87 references that are in the map. First posted on arXiv 6 Feb 2024.

Builds on

24 of 87 references.

References · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  3. 2021Convex optimizationcited by 2,479
  4. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
  5. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  6. 2019Symmetric Cross Entropy for Robust Learning with Noisy Labelscited by 499
  7. 2017Robust Loss Functions under Label Noise for Deep Neural Networkscited by 441
  8. 2021Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotationscited by 253
  9. 2019Are Anchor Points Really Indispensable in Label-Noise Learning?cited by 208
  10. 2023FITS: Modeling Time Series with 10k Parameterscited by 185
  11. 2019Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Ratescited by 142
  12. 2020Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learningcited by 142
12 more
  1. 2019Learning From Noisy Labels By Regularized Estimation Of Annotator Confusioncited by 126
  2. 2020A Survey of Label-noise Representation Learning: Past, Present and Futurecited by 121
  3. 2018Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendationcited by 101
  4. 2015Learning to Diagnose with LSTM Recurrent Neural Networkscited by 99
  5. 2021Provably End-to-end Label-Noise Learning without Anchor Pointscited by 97
  6. 2016Modeling Missing Data in Clinical Time Series with RNNscited by 80
  7. 2021Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularizationcited by 62
  8. 2022Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimationcited by 43
  9. 2020Improving Generalization by Controlling Label-Noise Information in Neural Network Weightscited by 35
  10. 2023Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosiscited by 30
  11. 2021Understanding Instance-Level Label Noise: Disparate Impacts and Treatmentscited by 22
  12. 2023Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Identifying and Correcting Label Noise for Robust GNNs via Influence Contradictioncited by 1
  2. 2026Weighted Temporal Decay Loss for Learning Wearable PPG Data with Sparse Clinical Labelscited by 0
  3. 2026Towards Multi-Source Domain Generalization for Sleep Staging with Noisy Labelscited by 0
  4. 2026TimeLAVA: Learning-Agnostic Valuation for Time Series Datacited by 0