Eric Dane dead at 53: Grey's Anatomy star dies after courageous battle with ALS... less than a year after announcing diagnosis RICHARD KAY: Andrew's fall may now be complete. The question is... Will he bring down the House of Windsor with him? Alysa Liu finally ends America's 24-year wait for a Winter Olympics figure skating gold medal as she wins nerve-shredding final The tide of sleaze rolling over Beatrice, Eugenie and Fergie is going to capsize them all. My stalker said he'd rape and dismember me. Then he turned his depraved sights on my seven-year-old daughter, says EVA LARUE.

We address data-driven learning of the infinitesimal generator of stochastic diffusion processes, essential for understanding numerical simulations of natural and physical systems.

We propose a probabilistic perspective on adversarial examples, allowing us to embed subjective understanding of semantics as a distribution into the process of generating adversarial examples, in a principled manner.

Noise in data significantly influences decision-making in the data science process. In fact, it has been shown that noise in data generation processes leads practitioners to find simpler models. However, an open question still remains: what is the degree of model simplification we can expect under different noise levels? In this work, we address this question by investigating the relationship between the amount of noise and model simplicity across various hypothesis spaces, focusing on decision trees and linear models. We formally show that noise acts as an implicit regularizer for several different noise models. Furthermore, we prove that Rashomon sets (sets of near-optimal models) constructed with noisy data tend to contain simpler models than corresponding Rashomon sets with non-noisy data. Additionally, we show that noise expands the set of "good" features and consequently enlarges the set of models that use at least one good feature. Our work offers theoretical guarantees and practical insights for practitioners and policymakers on whether simple-yet-accurate machine learning models are likely to exist, based on knowledge of noise levels in the data generation process.

The collected data in recommender systems generally suffers selection bias. Considerable works are proposed to address selection bias induced by observed user and item features, but they fail when hidden features (e.g., user age or salary) that affect