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Found 7 papers, 5 papers with code
Predicting from Strings: Language Model Embeddings for Bayesian Optimization
Bayesian Optimization is ubiquitous in the field of experimental design and blackbox optimization for improving search efficiency, but has been traditionally restricted to regression models which are only applicable to fixed search spaces and tabular input features.
The Vizier Gaussian Process Bandit Algorithm
Google Vizier has performed millions of optimizations and accelerated numerous research and production systems at Google, demonstrating the success of Bayesian optimization as a large-scale service.
OmniPred: Language Models as Universal Regressors
Regression is a powerful tool to accurately predict the outcome metric of a system given a set of parameters, but has traditionally been restricted to methods which are only applicable to a specific task.
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization
Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts.
Towards Learning Universal Hyperparameter Optimizers with Transformers
Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar distribution.
Neural Architecture Search for Energy Efficient Always-on Audio Models
Mobile and edge computing devices for always-on classification tasks require energy-efficient neural network architectures.
A Generalized Framework for Population Based Training
Population Based Training (PBT) is a recent approach that jointly optimizes neural network weights and hyperparameters which periodically copies weights of the best performers and mutates hyperparameters during training.
