mlsanityData to 7 Oct 2026

Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning

arXiv 1605.03852 ↗First posted 12 May 2016cs.CLcited by 22

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Abstract (arXiv)

We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features. The curricula are modeled by a linear ranking function which is the scalar product of a learned weight vector and an engineered feature vector that characterizes the different aspects of the complexity of each instance in the training corpus. We show that learning the curriculum improves performance on a variety of downstream tasks over random orders and in comparison to the natural corpus order.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 5; 2023: 3; 2024: 5; 2025: 3; 2026: 1.

22 papers.

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  1. 2021Curriculum Learning: A Surveycited by 257
  2. 2021Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learningcited by 35
  3. 2023Efficient Continual Pre-training for Building Domain Specific Large Language Modelscited by 33
  4. 2022The Effect of Task Ordering in Continual Learningcited by 30
  5. 2021Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimizationcited by 15
  6. 2022PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentationcited by 14
  7. 2022Data Selection Curriculum for Neural Machine Translationcited by 11
  8. 2024From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervisioncited by 10
  9. 2024Effective Generative AI: The Human-Algorithm Centaurcited by 8
  10. 2023Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systemscited by 6
  11. 2024A Psychology-based Unified Dynamic Framework for Curriculum Learningcited by 6
  12. 2022Self-directed Machine Learningcited by 3
10 more
  1. 2022Using Paraphrases to Study Properties of Contextual Embeddingscited by 3
  2. 2025Understanding Complexity in VideoQA via Visual Program Generationcited by 3
  3. 2021Analyzing Curriculum Learning for Sentiment Analysis along Task Difficulty, Pacing and Visualization Axescited by 2
  4. 2024Understanding the Training Speedup from Sampling with Approximate Lossescited by 2
  5. 2021Curriculum generation using Autoencoder based continuous optimizationcited by 0
  6. 2023Divergence-Based Domain Transferability for Zero-Shot Classificationcited by 0
  7. 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
  8. 2025Prototype-Guided Curriculum Learning for Zero-Shot Learningcited by 0
  9. 2025Beyond Repetition: Text Simplification and Curriculum Learning for Data-Constrained Pretrainingcited by 0
  10. 2026Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomycited by 0