LassoBench : A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
Šehić, Kenan LU
; Gramfort, Alexandre
; Salmon, Joseph
and Nardi, Luigi
LU
(2022)
1st International Conferenceon Automated Machine Learning, AutoML 2022
188.
- Abstract
While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in finance, genomics, and brain imaging applications, it is typically scarcely adopted due to its complex high-dimensional space composed by thousands of hyperparameters. On the other hand, the latest progress with high-dimensional hyperparameter optimization (HD-HPO) methods for black-box functions demonstrates that high-dimensional applications can indeed be efficiently optimized. Despite this initial success, HD-HPO approaches are mostly applied to synthetic problems with a moderate number of dimensions, which limits its impact in scientific and engineering applications. We propose LassoBench, the first benchmark... (More)
While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in finance, genomics, and brain imaging applications, it is typically scarcely adopted due to its complex high-dimensional space composed by thousands of hyperparameters. On the other hand, the latest progress with high-dimensional hyperparameter optimization (HD-HPO) methods for black-box functions demonstrates that high-dimensional applications can indeed be efficiently optimized. Despite this initial success, HD-HPO approaches are mostly applied to synthetic problems with a moderate number of dimensions, which limits its impact in scientific and engineering applications. We propose LassoBench, the first benchmark suite tailored for Weighted Lasso regression. LassoBench consists of benchmarks for both well-controlled synthetic setups (number of samples, noise level, ambient and effective dimensionalities, and multiple fidelities) and real-world datasets, which enables the use of many flavors of HPO algorithms to be studied and extended to the high-dimensional Lasso setting. We evaluate 6 state-of-the-art HPO methods and 3 Lasso baselines, and demonstrate that Bayesian optimization and evolutionary strategies can improve over the methods commonly used for sparse regression while highlighting limitations of these frameworks in very high-dimensional and noisy settings.
(Less)
- author
- Šehić, Kenan
LU
; Gramfort, Alexandre
; Salmon, Joseph
and Nardi, Luigi
LU
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of Machine Learning Research
- volume
- 188
- article number
- 189437
- conference name
- 1st International Conferenceon Automated Machine Learning, AutoML 2022
- conference location
- Baltimore, United States
- conference dates
- 2022-07-25 - 2022-07-27
- external identifiers
-
- scopus:85163568913
- language
- English
- LU publication?
- yes
- id
- 1dbfd9bc-fde6-4960-b83e-7343e7fd4228
- alternative location
- https://openreview.net/pdf?id=S4leJbTrLg5
- date added to LUP
- 2023-10-30 11:45:16
- date last changed
- 2023-12-19 16:06:35
@inproceedings{1dbfd9bc-fde6-4960-b83e-7343e7fd4228,
abstract = {{<p>While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in finance, genomics, and brain imaging applications, it is typically scarcely adopted due to its complex high-dimensional space composed by thousands of hyperparameters. On the other hand, the latest progress with high-dimensional hyperparameter optimization (HD-HPO) methods for black-box functions demonstrates that high-dimensional applications can indeed be efficiently optimized. Despite this initial success, HD-HPO approaches are mostly applied to synthetic problems with a moderate number of dimensions, which limits its impact in scientific and engineering applications. We propose LassoBench, the first benchmark suite tailored for Weighted Lasso regression. LassoBench consists of benchmarks for both well-controlled synthetic setups (number of samples, noise level, ambient and effective dimensionalities, and multiple fidelities) and real-world datasets, which enables the use of many flavors of HPO algorithms to be studied and extended to the high-dimensional Lasso setting. We evaluate 6 state-of-the-art HPO methods and 3 Lasso baselines, and demonstrate that Bayesian optimization and evolutionary strategies can improve over the methods commonly used for sparse regression while highlighting limitations of these frameworks in very high-dimensional and noisy settings.</p>}},
author = {{Šehić, Kenan and Gramfort, Alexandre and Salmon, Joseph and Nardi, Luigi}},
booktitle = {{Proceedings of Machine Learning Research}},
language = {{eng}},
title = {{LassoBench : A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso}},
url = {{https://openreview.net/pdf?id=S4leJbTrLg5}},
volume = {{188}},
year = {{2022}},
}