In order to create credit scores that provide utility and value, Credit2b's scores are on a scale of 0-100, with each point on this scale representing the probability of a positive outcome for the score. Therefore, a score of 80 simply means that there is an 80% probability that the company will pay on time for example. Other ratings agency scores are described in either tiers or bands that often cause confusion for credit practitioners who need to make important decisions quickly. With the tiered approach, two very similar companies may be scored in separate bands with completely different interpretations due to the randomness of the bands, and the simplicity of their data analysis algorithms. Machine-learning solutions deal with continuum, and give our customers information they can process and use quickly.

Rivasplata, Omar, Parrado-Hernandez, Emilio, Shawe-Taylor, John S., Sun, Shiliang, Szepesvari, Csaba

PAC-Bayes bounds have been proposed to get risk estimates based on a training sample. In this paper the PAC-Bayes approach is combined with stability of the hypothesis learned by a Hilbert space valued algorithm. The PAC-Bayes setting is used with a Gaussian prior centered at the expected output. Thus a novelty of our paper is using priors defined in terms of the data-generating distribution. Our main result estimates the risk of the randomized algorithm in terms of the hypothesis stability coefficients.

A team of researchers led by MIT's Katie Bouman have developed a new computer algorithm that could help astronomers generate the first true image of a black hole. At present, astronomers rely of imaginative minds of artists to create a clear image of a black hole. Black holes are very compact and are very far away from Earth, making it harder for astronomers to create a high-quality photo. "Taking a picture of the black hole in the center of the Milky Way galaxy is equivalent to taking an image of a grapefruit on the moon, but with a radio telescope," Bouman explained in a press release. For years, researchers have been using radio wavelengths to detect and explore distant objects due to the ability of radio frequencies to penetrate through galactic dust.

Hasselt, Hado van (Google DeepMind) | Guez, Arthur (Google DeepMind) | Silver, David (Google DeepMind)

The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games in the Atari 2600 domain. We then show that the idea behind the Double Q-learning algorithm, which was introduced in a tabular setting, can be generalized to work with large-scale function approximation. We propose a specific adaptation to the DQN algorithm and show that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games.

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data. For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code.