Search Results for author:
Found 5 papers, 3 papers with code
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization
This paper presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types.
Non-smooth Bayesian Optimization in Tuning Problems
By using this surrogate model, we want to capture the non-smoothness of the black-box function.
C-SAW: A Framework for Graph Sampling and Random Walk on GPUs
In this paper, we propose, to the best of our knowledge, the first GPU-based framework for graph sampling/random walk.
Graph Sampling
Distributed, Parallel, and Cluster Computing
Multitask and Transfer Learning for Autotuning Exascale Applications
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.
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction
Computational efficiency is an important consideration for deploying machine learning models for time series prediction in an online setting.
