Abstract Recent works using deep learning to solve routing problems such as the traveling salesman problem (TSP) have focused on construction heuristics. Such approaches find good quality solutions but require additional procedures beam search and sampling improve achieve state-of-the-art performance. However, few studies improvement heuristics, where a given solution is improved until reaching...

We have been developing tutors to help students learn programming concepts by solving problems. In this paper, we will discuss the use of problem-solving in Computer Science, the effectiveness of using problem-solving tutors to learn programming concepts, and the pedagogical relationship between solving problems and learning to write programs. We will also present the design and results from th...

Machine learning has been facing significant challenges over the last years, much of which stem from new characteristics machine problems, such as streaming data or incorporating human feedback into existing datasets and models. In these dynamic scenarios, change time models must adapt. However, do not necessarily mean patterns. The main goal this paper is to devise a method predict model's per...

Effective learning of user preferences is critical to easing user burden in various types of matching problems. Equally important is active query selection to further reduce the amount of preference information users must provide. We address the problem of active learning of user preferences for matching problems, introducing a novel method for determining probabilistic matchings, and developin...

Recently it was shown that the true sample complexity of active learning is asymptotically better than that for passive learning. In many interesting cases, the improvement has been shown to be exponential [BHW08]; however, there are artificial examples in which the improvement is small. In this paper we provide a basis for a exponential improvements in active learning. We show that exponential...

This paper explains why learning to solve constraint problems is so difficult, and describes a set of methods that has been effective on a broad variety of problem classes. The primary focus is on learning an effective search algorithm as a weighted subset of ordering heuristics. Experiments show the impact of several novel techniques on a variety of problems. When a planning problem is cast as...

e-learning model is examined of three major dimensions. and each dimension has a range of indicators that is effective in optimization and modeling, in many optimization problems in the modeling, target function or constraints may change over time that as a result optimization of these problems can also be changed. if any of these undetermined events be considered in the optimization process, t...