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hyperparameter learning

19 papers

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Co-occurring keywords

gaussian process (1198) bilevel optimization (193) variational inference (2069) hyperparameter optimization (405) marginal likelihood (75) gradient descent (1143) conjugate gradient (43) recurrent neural network (1790) kernel methods (1096) gradient-based optimization (191)

Papers

UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning CVPR 2025
Adaptive Hyper-parameter Learning for Deep Semantic Retrieval EMNLP 2023
Bayesian Model Selection, the Marginal Likelihood, and Generalization ICML 2022
On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization JMLR 2021
Bias-Free Scalable Gaussian Processes via Randomized Truncations ICML 2021
SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization NIPS 2021
A Gradient Method for Multilevel Optimization NIPS 2021
Dual Parameterization of Sparse Variational Gaussian Processes NIPS 2021
Infinite Task Learning in RKHSs AISTATS 2019
HyperAdam: A Learnable Task-Adaptive Adam for Network Training AAAI 2019
Fast Automatic Smoothing for Generalized Additive Models JMLR 2019
Hyperparameter Learning via Distributional Transfer NIPS 2019
Infinite-Horizon Gaussian Processes NIPS 2018
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models AISTATS 2018
Streaming Sparse Gaussian Process Approximations NIPS 2017
Preconditioning Kernel Matrices ICML 2016
Efficient multiple hyperparameter learning for log-linear models NIPS 2007
Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms NIPS 2006
Gaussian and Wishart Hyperkernels NIPS 2006