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.

We present a concrete design for Solomonoff’s incremental machine learning system suitable for desktop computers. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on a stochastic Context Free Grammar together with new update algorithms that use the same grammar as a guiding probability distribution for incremental machine learning. The updates include adjusting production probabilities, re-using previous solutions, learning programming idioms and discovery of frequent subprograms. The issues of extending the a priori probability distribution and bootstrapping are discussed. We have implemented a good portion of the proposed algorithms. Experiments with toy problems show that the update algorithms work as expected.
Gigamachine: incremental machine learning on desktop computers

Finding inclusion-minimal hitting sets for a given collection of sets is a fundamental combinatorial problem with applications in domains as diverse as Boolean algebra, computational biology, and data mining. Much of the algorithmic literature focuses on the problem of recognizing the collection of minimal hitting sets; however, in many of the applications, it is more important to generate these hitting sets. We survey twenty algorithms from across a variety of domains, considering their history, classification, useful features, and computational performance on a variety of synthetic and real-world inputs. We also provide a suite of implementations of these algorithms with a ready-to-use, platform-agnostic interface based on Docker containers and the AlgoRun framework, so that interested computational scientists can easily perform similar tests with inputs from their own research areas on their own computers or through a convenient Web interface.
The minimal hitting set generation problem: algorithms and computation

To be considered for the 2017 IEEE Jack Keil Wolf ISIT Student Paper Award. Interaction information is one of the multivariate generalizations of mutual information, which expresses the amount information shared among a set of variables, beyond the information, which is shared in any proper subset of those variables. Unlike (conditional) mutual information, which is always non-negative, interaction information can be negative. We utilize this property to find the direction of causal influences among variables in a triangle topology under some mild assumptions.

Interaction Information for Causal Inference: The Case of Directed Triangle