instruction:
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.
question:
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.
answer:
Convex Learning of Multiple Tasks and their Structure


question:
Topic models provide a useful method for dimensionality reduction and exploratory data<lb>analysis in large text corpora. Most approaches to topic model inference have been based on<lb>a maximum likelihood objective. Efficient algorithms exist that approximate this objective,<lb>but they have no provable guarantees. Recently, algorithms have been introduced that provide<lb>provable bounds, but these algorithms are not practical because they are inefficient and not ro-<lb>bust to violations of model assumptions. In this paper we present an algorithm for topic model<lb>inference that is both provable and practical. The algorithm produces results comparable to the<lb>best MCMC implementations while running orders of magnitude faster.
answer:
A Practical Algorithm for Topic Modeling with Provable Guarantees


question:
The main goal of this paper is to describe a new pruning method for solving decision trees and game trees. The pruning method for decision trees suggests a slight variant of decision trees that we call scenario trees. In scenario trees, we do not need a conditional probability for each edge emanating from a chance node. Instead, we require a joint probability for each path from the root node to a leaf node. We compare the pruning method to the traditional rollback method for decision trees and game trees. For problems that require Bayesian revision of probabilities, a scenario tree representation with the pruning method is more efficient than a decision tree representation with the rollback method. For game trees, the pruning method is more efficient than the rollback method.
answer:

A New Pruning Method for Solving Decision Trees and Game Trees