hyperparameter learning

Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functions ICLR 2025 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 A Gradient Method for Multilevel Optimization NeurIPS 2021 Bias-Free Scalable Gaussian Processes via Randomized Truncations ICML 2021 Dual Parameterization of Sparse Variational Gaussian Processes NeurIPS 2021 On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization JMLR 2021 SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization NeurIPS 2021 Fast Automatic Smoothing for Generalized Additive Models JMLR 2019 HyperAdam: A Learnable Task-Adaptive Adam for Network Training AAAI 2019 Hyperparameter Learning via Distributional Transfer NeurIPS 2019 Infinite Task Learning in RKHSs AISTATS 2019 Online Hyper-Parameter Learning for Auto-Augmentation Strategy ICCV 2019 Infinite-Horizon Gaussian Processes NeurIPS 2018 Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models AISTATS 2018 Streaming Sparse Gaussian Process Approximations NeurIPS 2017 Preconditioning Kernel Matrices ICML 2016 Gaussian Process Training with Input Noise NeurIPS 2011 Efficient multiple hyperparameter learning for log-linear models NeurIPS 2007 Gaussian and Wishart Hyperkernels NeurIPS 2006 Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms NeurIPS 2006