mlsanityData to 7 Oct 2026

Hyperparameter Learning via Distributional Transfer

arXiv 1810.06305 ↗First posted 15 Oct 2018stat.ML cs.LGcited by 13

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Abstract (arXiv)

Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We propose to transfer information across tasks using learnt representations of training datasets used in those tasks. This results in a joint Gaussian process model on hyperparameters and data representations. Representations make use of the framework of distribution embeddings into reproducing kernel Hilbert spaces. The developed method has a faster convergence compared to existing baselines, in some cases requiring only a few evaluations of the target objective.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Oct 2018.

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Citing papers per year: 2021: 5; 2022: 1; 2023: 3; 2024: 2; 2025: 2.

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  1. 2023AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Riskscited by 34
  2. 2023Transfer Learning for Bayesian Optimization: A Surveycited by 27
  3. 2021Deconditional Downscaling with Gaussian Processescited by 24
  4. 2021Meta-Learning Reliable Priors in the Function Spacecited by 23
  5. 2021Hyperparameter Transfer Learning with Adaptive Complexitycited by 17
  6. 2024Monte Carlo Tree Search based Space Transfer for Black-box Optimizationcited by 7
  7. 2023FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulationcited by 5
  8. 2024An Overview of Causal Inference using Kernel Embeddingscited by 5
  9. 2021Amortized Auto-Tuning: Cost-Efficient Bayesian Transfer Optimization for Hyperparameter Recommendationcited by 4
  10. 2021Hyperparameter Optimization with Differentiable Metafeaturescited by 3
  11. 2025None To Optima in Few Shots: Bayesian Optimization with MDP Priorscited by 3
  12. 2025Provable Accelerated Bayesian Optimization with Knowledge Transfercited by 1
1 more
  1. 2022AODisaggregation: toward global aerosol vertical profilescited by 0