A chain rule for the expected suprema of Gaussian processes
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Abstract (arXiv)
The expected supremum of a Gaussian process indexed by the image of an index set under a function class is bounded in terms of separate properties of the index set and the function class. The bound is relevant to the estimation of nonlinear transformations or the analysis of learning algorithms whenever hypotheses are chosen from composite classes, as is the case for multi-layer models.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Nov 2014.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 6; 2024: 2; 2025: 2; 2026: 1.
13 papers.
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- 2024Sharing Knowledge in Multi-Task Deep Reinforcement Learningcited by 42
- 2023A Theory of Multimodal Learningcited by 25
- 2021Learning an Explicit Hyperparameter Prediction Function Conditioned on Taskscited by 9
- 2023Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalizationcited by 8
- 2023Nonlinear Meta-Learning Can Guarantee Faster Ratescited by 8
- 2024On Generalization Bounds for Neural Networks with Low Rank Layerscited by 4
- 2023Provable Pathways: Learning Multiple Tasks over Multiple Pathscited by 3
- 2023Statistical learning on measures: an application to persistence diagramscited by 2
- 2021Deep Safe Multi-Task Learningcited by 1
- 2023A Chain Rule for the Expected Suprema of Bernoulli Processescited by 1
- 2025Bridging Lifelong and Multi-Task Representation Learning via Algorithm and Complexity Measurecited by 0
- 2025Representative Action Selection for Large Action Space: From Bandits to MDPscited by 0
- 2026Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Trees28 Jun 2026
- 2025Representative Action Selection for Large Action Space: From Bandits to MDPs27 Nov 2025
- 2025Bridging Lifelong and Multi-Task Representation Learning via Algorithm and Complexity Measure3 Nov 2025
- 2024On Generalization Bounds for Neural Networks with Low Rank Layers20 Nov 2024
- 2024Sharing Knowledge in Multi-Task Deep Reinforcement Learning17 Jan 2024
- 2023A Theory of Multimodal Learning21 Sep 2023
- 2023Nonlinear Meta-Learning Can Guarantee Faster Rates20 Jul 2023
- 2023Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization17 May 2023
- 2023A Chain Rule for the Expected Suprema of Bernoulli Processes27 Apr 2023
- 2023Statistical learning on measures: an application to persistence diagrams15 Mar 2023
- 2023Provable Pathways: Learning Multiple Tasks over Multiple Paths8 Mar 2023
- 2021Deep Safe Multi-Task Learning20 Nov 2021
1 more
- 2024Sharing Knowledge in Multi-Task Deep Reinforcement Learningcited by 42
- 2023A Theory of Multimodal Learningcited by 25
- 2021Learning an Explicit Hyperparameter Prediction Function Conditioned on Taskscited by 9
- 2023Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalizationcited by 8
- 2023Nonlinear Meta-Learning Can Guarantee Faster Ratescited by 8
- 2024On Generalization Bounds for Neural Networks with Low Rank Layerscited by 4
- 2023Provable Pathways: Learning Multiple Tasks over Multiple Pathscited by 3
- 2023Statistical learning on measures: an application to persistence diagramscited by 2
- 2021Deep Safe Multi-Task Learningcited by 1
- 2023A Chain Rule for the Expected Suprema of Bernoulli Processescited by 1
- 2025Bridging Lifelong and Multi-Task Representation Learning via Algorithm and Complexity Measurecited by 0
- 2025Representative Action Selection for Large Action Space: From Bandits to MDPscited by 0
12 most cited of 13 All 13 (JSON)
- 2026Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Trees28 Jun 2026
- 2025Representative Action Selection for Large Action Space: From Bandits to MDPs27 Nov 2025
- 2025Bridging Lifelong and Multi-Task Representation Learning via Algorithm and Complexity Measure3 Nov 2025
- 2024On Generalization Bounds for Neural Networks with Low Rank Layers20 Nov 2024
- 2024Sharing Knowledge in Multi-Task Deep Reinforcement Learning17 Jan 2024
- 2023A Theory of Multimodal Learning21 Sep 2023
- 2023Nonlinear Meta-Learning Can Guarantee Faster Rates20 Jul 2023
- 2023Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization17 May 2023
- 2023A Chain Rule for the Expected Suprema of Bernoulli Processes27 Apr 2023
- 2023Statistical learning on measures: an application to persistence diagrams15 Mar 2023
- 2023Provable Pathways: Learning Multiple Tasks over Multiple Paths8 Mar 2023
- 2021Deep Safe Multi-Task Learning20 Nov 2021
12 newest of 13 All 13 (JSON)