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Found 8 papers, 2 papers with code
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach
The rising growth of deep neural networks (DNNs) and datasets in size motivates the need for efficient solutions for simultaneous model selection and training.
Direct loss minimization algorithms for sparse Gaussian processes
The application of DLM in non-conjugate cases is more complex because the logarithm of expectation in the log-loss DLM objective is often intractable and simple sampling leads to biased estimates of gradients.
Weighted Meta-Learning
Meta-learning leverages related source tasks to learn an initialization that can be quickly fine-tuned to a target task with limited labeled examples.
Pseudo-Bayesian Learning via Direct Loss Minimization with Applications to Sparse Gaussian Process Models
Our criterion can be used to derive new sparse Gaussian process algorithms that have error guarantees applicable to various likelihoods.
Feature Gradients: Scalable Feature Selection via Discrete Relaxation
In this paper we introduce Feature Gradients, a gradient-based search algorithm for feature selection.
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models
The paper furthers such analysis by providing bounds on the excess risk of variational inference algorithms and related regularized loss minimization algorithms for a large class of latent variable models with Gaussian latent variables.
Probabilistic Matrix Factorization for Automated Machine Learning
Automating the selection and tuning of machine learning pipelines consisting of data pre-processing methods and machine learning models, has long been one of the goals of the machine learning community.
Monte Carlo Structured SVI for Two-Level Non-Conjugate Models
The stochastic variational inference (SVI) paradigm, which combines variational inference, natural gradients, and stochastic updates, was recently proposed for large-scale data analysis in conjugate Bayesian models and demonstrated to be effective in several problems.
