In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: Reducing the amount of human supervision is a key problem in machine learning and a natural approach is that of exploiting the relations (structure) among different tasks. This is the idea at the core of multi-task learning. In this context a fundamental question is how to incorporate the tasks structure in the learning problem. We tackle this question by studying a general computational framework that allows to encode a-priori knowledge of the tasks structure in the form of a convex penalty; in this setting a variety of previously proposed methods can be recovered as special cases, including linear and non-linear approaches. Within this framework, we show that tasks and their structure can be efficiently learned considering a convex optimization problem that can be approached by means of block coordinate methods such as alternating minimization and for which we prove convergence to the global minimum.
[EX A]: Convex Learning of Multiple Tasks and their Structure

[EX Q]: How to build a machine learning method that can continuously gain structured visual knowledge by learning structured facts? Our goal in this paper is to address this question by proposing a problem setting, where training data comes as structured facts in images with different types including (1) objects(e.g., <boy>), (2) attributes (e.g., <boy,tall>), (3) actions (e.g., <boy, playing>), (4) interactions (e.g., <boy, riding, a horse >). Each structured fact has a semantic language view (e.g., < boy, playing>) and a visual view (an image with this fact). A human is able to efficiently gain visual knowledge by learning facts in a never ending process, and as we believe in a structured way (e.g., understanding "playing" is the action part of < boy, playing>, and hence can generalize to recognize <girl, playing > if just learn <girl> additionally). Inspired by human visual perception, we propose a model that is (1) able to learn a representation, we name as wild-card, which covers different types of structured facts, (2) could flexibly get fed with structured fact language-visual view pairs in a never ending way to gain more structured knowledge, (3) could generalize to unseen facts, and (4) allows retrieval of both the fact language view given the visual view (i.e., image) and vice versa. We also propose a novel method to generate hundreds of thousands of structured fact pairs from image caption data, which are necessary to train our model and can be useful for other applications.
[EX A]: SHERLOCK: MODELING STRUCTURED KNOWLEDGE IN IMAGES

[EX Q]: <lb>In applications such as recommendation systems and revenue management, it is important to<lb>predict preferences on items that have not been seen by a user or predict outcomes of comparisons<lb>among those that have never been compared. A popular discrete choice model of multinomial<lb>logit model captures the structure of the hidden preferences with a low-rank matrix. In order to<lb>predict the preferences, we want to learn the underlying model from noisy observations of the<lb>low-rank matrix, collected as revealed preferences in various forms of ordinal data. A natural<lb>approach to learn such a model is to solve a convex relaxation of nuclear norm minimization.<lb>We present the convex relaxation approach in two contexts of interest: collaborative ranking<lb>and bundled choice modeling. In both cases, we show that the convex relaxation is minimax<lb>optimal. We prove an upper bound on the resulting error with finite samples, and provide a<lb>matching information-theoretic lower bound.
[EX A]:
Collaboratively Learning Preferences from Ordinal Data