Controlling False Discovery Rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science. Existing methods rely on the test-level covariates while ignoring metrics about test-level covariates. This strategy may not be optimal for complex large-scale problems, where indirect relations often exist among test-level covariates and auxiliary metrics or covariates. We incorporate auxiliary covariates among test-level covariates in a deep Black-Box framework (named as NeurT-FDR) which boosts statistical power and controls FDR for multiple hypothesis testing. Our method parametrizes the test-level covariates as a neural network and adjusts the auxiliary covariates through a regression framework, which enables flexible handling of high-dimensional features as well as efficient end-to-end optimization. We show that NeurT-FDR makes substantially more discoveries in three real datasets compared to competitive baselines.

We propose an approach for comparing curricula of study programs in higher education.Pre-trained word embeddings are fine-tuned in a study program classification task, where each curriculum is represented by the names and content of its courses.By combining metric learning with a novel course-guided attention mechanism, our method obtains more accurate curriculum representations than strong baselines.Experiments on a new dataset with curricula of computing programs demonstrate the intuitive power of our approach via attention weights, topic modeling, and embeddings visualizations.We also present a use case comparing computing curricula from USA and Latin America to showcase the capabilities of our improved embeddings representations.

Existing captioning and gaze prediction approaches do not consider the multiple facets of personality that affect how a viewer extracts meaning from an image. While there are methods that consider personalized captioning, they do not consider personalized perception across modalities, i.e. how a person's way of looking at an image (gaze) affects the way they describe it (captioning). In this work, we propose a model for modeling cross-modality personalized retrieval. In addition to modeling gaze and captions, we also explicitly model the personality of the users providing these samples. We incorporate constraints that encourage gaze and caption samples on the same image to be close in a learned space; we refer to this as content modeling. We also model style: we encourage samples provided by the same user to be close in a separate embedding space, regardless of the image on which they were provided. To leverage the complementary information that content and style constraints provide, we combine the embeddings from both networks. We show that our combined embeddings achieve better performance than existing approaches for cross-modal retrieval.

While semantic visual attributes have been shown useful for a variety of tasks, many attributes are difficult to model computationally. One of the reasons for this difficulty is that it is not clear where in an image the attribute lives. We propose to tackle this problem by involving humans more directly in the process of learning an attribute model. We ask humans to examine a set of images to determine if a given attribute is present in them, and we record where they looked. We create gaze maps for each attribute, and use these gaze maps to improve attribute prediction models. For test images we do not have gaze maps available, so we predict them based on models learned from collected gaze maps for each attribute of interest. Compared to six baselines, we improve prediction accuracies on attributes of faces and shoes, and we show how our method might be adapted for scene images. We demonstrate additional uses of our gaze maps for visualization of attribute models and learning "schools of thought" between users in terms of their understanding of the attribute.