mlsanityData to 14 Oct 2026

Deep Kernel Learning

arXiv 1511.02222 ↗First posted 6 Nov 2015cs.LG cs.AI stat.MEcited by 445

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Abstract (arXiv)

We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods. Specifically, we transform the inputs of a spectral mixture base kernel with a deep architecture, using local kernel interpolation, inducing points, and structure exploiting (Kronecker and Toeplitz) algebra for a scalable kernel representation. These closed-form kernels can be used as drop-in replacements for standard kernels, with benefits in expressive power and scalability. We jointly learn the properties of these kernels through the marginal likelihood of a Gaussian process. Inference and learning cost O(n) for n training points, and predictions cost O(1) per test point. On a large and diverse collection of applications, including a dataset with 2 million examples, we show improved performance over scalable Gaussian processes with flexible kernel learning models, and stand-alone deep architectures.

Key facts. Cited by 445 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Nov 2015.

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Citing papers per year: 2020: 1; 2021: 68; 2022: 82; 2023: 75; 2024: 73; 2025: 77; 2026: 69.

445 papers.

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  1. 2024Large Language Models to Enhance Bayesian Optimizationcited by 135
  2. 2021Solving and Learning Nonlinear PDEs with Gaussian Processescited by 129
  3. 2021Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learningcited by 113
  4. 2021DEUP: Direct Epistemic Uncertainty Predictioncited by 104
  5. 2023Neural Kernel Surface Reconstructioncited by 104
  6. 2022Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoderscited by 102
  7. 2021Priors in Bayesian Deep Learning: A Reviewcited by 101
  8. 2021On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertaintycited by 87
  9. 2021The Promises and Pitfalls of Deep Kernel Learningcited by 81
  10. 2023A Survey on Uncertainty Quantification Methods for Deep Learningcited by 81
  11. 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 79
  12. 2021Personalized Federated Learning with Gaussian Processescited by 76
38 more
  1. 2023TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023cited by 74
  2. 2021Few-Shot Bayesian Optimization with Deep Kernel Surrogatescited by 71
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  4. 2024Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AIcited by 62
  5. 2021Neural Fields as Learnable Kernels for 3D Reconstructioncited by 58
  6. 2022Bayesian Model Selection, the Marginal Likelihood, and Generalizationcited by 57
  7. 2025Deep Learning is Not So Mysterious or Differentcited by 56
  8. 2021High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learningcited by 54
  9. 2022Neural Diffusion Processescited by 50
  10. 2023A Study of Bayesian Neural Network Surrogates for Bayesian Optimizationcited by 50
  11. 2022A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awarenesscited by 49
  12. 2024A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?cited by 48
  13. 2022On Uncertainty, Tempering, and Data Augmentation in Bayesian Classificationcited by 46
  14. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  15. 2022Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximationscited by 42
  16. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 41
  17. 2022The Unreasonable Effectiveness of Deep Evidential Regressioncited by 40
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  19. 2023Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?cited by 36
  20. 2024Diffusion Models as Constrained Samplers for Optimization with Unknown Constraintscited by 35
  21. 2021Fast Adaptation with Linearized Neural Networkscited by 32
  22. 2022Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functionscited by 32
  23. 2023Sparse Cholesky Factorization for Solving Nonlinear PDEs via Gaussian Processescited by 31
  24. 2021Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Datacited by 28
  25. 2021Kernel Continual Learningcited by 27
  26. 2022NeuralEF: Deconstructing Kernels by Deep Neural Networkscited by 27
  27. 2023Transfer Learning for Bayesian Optimization: A Surveycited by 27
  28. 2023Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorialcited by 27
  29. 2023A Primer on Bayesian Neural Networks: Review and Debatescited by 27
  30. 2021GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learningcited by 26
  31. 2022NP-Match: When Neural Processes meet Semi-Supervised Learningcited by 25
  32. 2024Kernel PCA for Out-of-Distribution Detectioncited by 24
  33. 2021Multivariate Deep Evidential Regressioncited by 23
  34. 2021Meta-Learning Reliable Priors in the Function Spacecited by 23
  35. 2022Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentationcited by 23
  36. 2022The Neural Process Family: Survey, Applications and Perspectivescited by 23
  37. 2022Integrating Random Effects in Deep Neural Networkscited by 22
  38. 2022Geometric Knowledge Distillation: Topology Compression for Graph Neural Networkscited by 22

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