Statistical Learning
Kernel PCA for Out-of-Distribution Detection Kun Fang
Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs).
ChronoEpilogi: Scalable Time-Series Variable Selection with Multiple Solutions
Lasso performance, with the added benefit of providing multiple solutions.
Certified Adversarial Robustness via Randomized ฮฑ-Smoothing for Regression Models
Certified adversarial robustness of large-scale deep networks has progressed substantially after the introduction of randomized smoothing.
SAND: Smooth Imputation Of Sparse And Noisy Functional Data With Transformer Networks
The conventional approach to imputing longitudinal data is to employ a parametric model with mixed-effects, such as the linear mixed-effects model.
NE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction
Despite its broad applications in fields such as computer vision, graph learning, and natural language processing, the development of a data projection model that can be effectively used to visualize data in the context of FL is crucial yet remains heavily under-explored. Neighbor embedding (NE) is an essential technique for visualizing complex high-dimensional data, but collab-oratively learning a joint NE model is difficult.
Off-policy estimation with adaptively collected data: the power of online learning
Off-policy estimation: Oftentimes, one needs to estimate the linear functional based on observational data collected from a behavior policy.
EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Models
Nevertheless, it encounters challenges related to ill-posed problems, resulting in deviations between single model predictions and ground-truths. Ensemble learning, as a powerful machine learning technique, aims to address these deviations by combining the predictions of multiple base models.
