Gradient Learning under Tilted Empirical Risk Minimization.

Gradient Learning (GL), aiming to estimate the gradient of target function, has attracted much attention in variable selection problems due to its mild structure requirements and wide applicability. Despite rapid progress, the majority of the existing GL works are based on the empirical risk minimization (ERM) principle, which may face the degraded performance under complex data environment, e.g.

Error Bound of Mode-Based Additive Models.

Due to their flexibility and interpretability, additive models are powerful tools for high-dimensional mean regression and variable selection. However, the least-squares loss-based mean regression models suffer from sensitivity to non-Gaussian noises, and there is also a need to improve the model's robustness. This paper considers the estimation and variable selection via modal regression in reproducing kernel Hilbert spaces (RKHSs).

Robust Variable Selection and Estimation Based on Kernel Modal Regression.

Model-free variable selection has attracted increasing interest recently due to its flexibility in algorithmic design and outstanding performance in real-world applications. However, most of the existing statistical methods are formulated under the mean square error (MSE) criterion, and susceptible to non-Gaussian noise and outliers. As the MSE criterion requires the data to satisfy Gaussian noise condition, it potentially hampers the effectiveness of model-free methods in complex circumstances.

Atomic force microscopy-guided fractionation reveals the influence of cranberry phytochemicals on adhesion of Escherichia coli.

Cranberry juice has been long used to prevent infections because of its effect on the adhesion of the bacteria to the host surface. Proanthocyanidins (PACs) comprise of one of the major classes of phytochemicals found in cranberry, which have been extensively studied and found effective in combating adhesion of pathogenic bacteria. The role of other cranberry constituents in impacting bacterial adhesion haven't been studied very well.