9 citations · 24 across the 5 of their papers we have counts for
4 papers · 1 filter
Non-smooth Bayesian Optimization in Tuning Problems
Hengrui Luo, James W. Demmel, Younghyun Cho +2
Building surrogate models is one common approach when we attempt to learn unknown black-box functions. Bayesian optimization provides a framework which allows us to build surrogate…
Multitask and Transfer Learning for Autotuning Exascale Applications
Wissam M. Sid-Lakhdar, Mohsen Mahmoudi Aznaveh, Xiaoye S. Li +1
Multitask learning and transfer learning have proven to be useful in the field of machine learning when additional knowledge is available to help a prediction task. We aim at deriv…
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction
Hongyuan Zhan, Gabriel Gomes, Xiaoye S. Li +2
Computational efficiency is an important consideration for deploying machine learning models for time series prediction in an online setting. Machine learning algorithms adjust mod…
A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression
Elizaveta Rebrova, Gustavo Chavez, Yang Liu +2
We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Using hierarchical matrix approximations for the kernel matrix the memory requireme…