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alocv
GitHub arXiv Blog post
alocv is an R package that implement the approximate leave-one-out (ALO) cross-validation strategy for common regressors in an efficient way. Leave-one-out cross-validation (LOOCV) is an appealing method for parameter tuning. However, its high computational cost (requiring fitting the model $n$ times) often makes it infeasible in application. Our proposed method approximates the LOOCV estimations using only the full data fit and siginificantly reduced the time needed for risk estimation.
<h2>publications</h2>
orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization
Published in The R Journal, 2019
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
Published in INFORMS Data Mining and Decision Analysis Workshop, 2019
Generative quantum machine learning via denoising diffusion probabilistic models
Published in Physical Review Letters, 2024
Holographic deep thermalization: theory and experimentation
Published in TBA, 2025
<h2>talks</h2>
<h2>teaching</h2>
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.
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