19 citations · 33 across the 8 of their papers we have counts for
8 papers
Performance Prediction for Large Systems via Text-to-Text Regression
Yash Akhauri, Bryan Lewandowski, Cheng-Hsi Lin +7
In many industries, predicting metric outcomes of large systems is a fundamental problem, driven largely by traditional tabular regression. However, such methods struggle on comple…
Language Model Embeddings Can Be Sufficient for Bayesian Optimization
Tung Nguyen, Qiuyi Zhang, Bangding Yang +6
Bayesian Optimization is ubiquitous in experimental design and black-box optimization for improving search efficiency. However, most existing approaches rely on regression models w…
The Vizier Gaussian Process Bandit Algorithm
Xingyou Song, Qiuyi Zhang, Chansoo Lee +8
Google Vizier has performed millions of optimizations and accelerated numerous research and production systems at Google, demonstrating the success of Bayesian optimization as a la…
OmniPred: Language Models as Universal Regressors
Xingyou Song, Oscar Li, Chansoo Lee +4
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 applic…
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization
Xingyou Song, Sagi Perel, Chansoo Lee +2
Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts. To operate at the sc…
Towards Learning Universal Hyperparameter Optimizers with Transformers
Yutian Chen, Xingyou Song, Chansoo Lee +9
Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar…
Neural Architecture Search for Energy Efficient Always-on Audio Models
Daniel T. Speckhard, Karolis Misiunas, Sagi Perel +3
Mobile and edge computing devices for always-on classification tasks require energy-efficient neural network architectures. In this paper we present several changes to neural archi…
A Generalized Framework for Population Based Training
Ang Li, Aleksandra Spyra, Sagi Perel +6
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…