Subset selection with shrinkage: Sparse linear modeling when the SNR is low
We study a seemingly unexpected and relatively less understood overfitting aspect of a
fundamental tool in sparse linear modeling—best subset selection—which minimizes the …
fundamental tool in sparse linear modeling—best subset selection—which minimizes the …
Cardinality minimization, constraints, and regularization: a survey
We survey optimization problems that involve the cardinality of variable vectors in
constraints or the objective function. We provide a unified viewpoint on the general problem …
constraints or the objective function. We provide a unified viewpoint on the general problem …
Osscar: One-shot structured pruning in vision and language models with combinatorial optimization
L0learn: A scalable package for sparse learning using l0 regularization
We present L0Learn: an open-source package for sparse linear regression and
classification using ℓ0 regularization. L0Learn implements scalable, approximate …
classification using ℓ0 regularization. L0Learn implements scalable, approximate …
A minimax optimal approach to high-dimensional double sparse linear regression
In this paper, we focus our attention on the high-dimensional double sparse linear
regression, that is, a combination of element-wise and group-wise sparsity. To address this …
regression, that is, a combination of element-wise and group-wise sparsity. To address this …
Analysis of influencing factors of traffic accidents on urban ring road based on the SVM model optimized by Bayesian method
L Wang, M **ao, J Lv, J Liu - PLoS One, 2024 - journals.plos.org
Based on small scale sample of accident data from specific scenarios, fully exploring the
potential influencing factors of the severity of traffic accidents has become a key and …
potential influencing factors of the severity of traffic accidents has become a key and …
[HTML][HTML] Optimal forecast reconciliation with time series selection
Forecast reconciliation ensures forecasts of time series in a hierarchy adhere to aggregation
constraints, enabling aligned decision making. While forecast reconciliation can enhance …
constraints, enabling aligned decision making. While forecast reconciliation can enhance …