We propose a distributionally robust optimization formulation with Wasserstein-based uncertainty set for selecting grouped variables under perturbations on the data both linear regression and classification problems. The resulting model offers robustness explanations least absolute shrinkage selection operator algorithms highlights connection between regularization. prove probabilistic bounds o...

Convolutional networks have achieved great success in various vision tasks. This is mainly due to a considerable amount of research on network structure. In this study, instead focusing architectures, we focused the convolution unit itself. The existing has fixed shape and limited observing restricted receptive fields. earlier work, proposed active (ACU), which can freely define its learn by pa...

Word embeddings are a powerful approach for analyzing language, and exponential family embeddings (EFE) extend them to other types of data. Here we develop structured exponential family embeddings (S-EFE), a method for discovering embeddings that vary across related groups of data. We study how the word usage of U.S. Congressional speeches varies across states and party affiliation, how words a...

The repetition blindness (RB) effect demonstrates that people often fail to detect the second presentation of an identical object (e.g., Kanwisher, 1987). Grouping of identical items is a well-documented perceptual phenomenon, and this grouping generally facilitates perception. These two effects pose a puzzle: RB impairs perception, while perceptual grouping improves it. Here, we combined these...

Elimination of quantifiers from formulae of classical first-order logic is a process with many implications in automated deduction [6, 1] and in foundational issues [4, 7]. When no particular theory is considered, quantifiers are usually eliminated by adopting Skolemization or the ε-operator. Traditionally, Skolemization and the ε-symbol have different, if not complementary, employments. Skolem...

We consider estimation of nonlinear panel data models with common and individual specific parameters. Fixed effects estimators are known to suffer from the incidental parameters problem, which can lead to large biases in estimates of common parameters. Pooled estimators, which ignore heterogeneity across individuals, are also generally inconsistent. We assume that individuals in our data are gr...

We consider regression models for multiple correlated outcomes, where the outcomes are nested in domains. We show that random effect models for this nested situation fit into a standard factor model framework, which leads us to view the modeling options as a spectrum between parsimonious random effect multiple outcomes models and more general continuous latent factor models. We introduce a set ...