Early identification of mood episodes enabling timely mood stabilisation is an important clinical goal. Recent technological advances allow the prospective reporting of mood in real time enabling more accurate, efficient data capture. The complex nature of these data streams in combination with challenge of deriving meaning from missing data mean pose a significant analytic challenge. The signature method is derived from stochastic analysis and has the ability to capture important properties of complex ordered time series data. Objective: To explore whether the onset of episodes of mania and depression can be identified using self-reported mood data.

The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention. Such automatic (aML) approaches show impressive success. Recent results even demonstrate intriguingly that deep learning applied for automatic classification of skin lesions is on par with the performance of dermatologists, yet outperforms the average. As human perception is inherently limited, such approaches can discover patterns, e.g. that two objects are similar, in arbitrarily high-dimensional spaces what no human is able to do. Humans can deal only with limited amounts of data, whilst big data is beneficial for aML; however, in health informatics, we are often confronted with a small number of data sets, where aML suffer of insufficient training samples and many problems are computationally hard. Here, interactive machine learning (iML) may be of help, where a human-in-the-loop contributes to reduce the complexity of NP-hard problems. A further motivation for iML is that standard black-box approaches lack transparency, hence do not foster trust and acceptance of ML among end-users. Rising legal and privacy aspects, e.g. with the new European General Data Protection Regulations, make black-box approaches difficult to use, because they often are not able to explain why a decision has been made. In this paper, we present some experiments to demonstrate the effectiveness of the human-in-the-loop approach, particularly in opening the black-box to a glass-box and thus enabling a human directly to interact with an learning algorithm. We selected the Ant Colony Optimization framework, and applied it on the Traveling Salesman Problem, which is a good example, due to its relevance for health informatics, e.g. for the study of protein folding. From studies of how humans extract so much from so little data, fundamental ML-research also may benefit.

We introduce a notion of algorithmic stability of learning algorithms---that we term \emph{argument stability}---that captures stability of the hypothesis output by the learning algorithm in the normed space of functions from which hypotheses are selected. The main result of the paper bounds the generalization error of any learning algorithm in terms of its argument stability. The bounds are based on martingale inequalities in the Banach space to which the hypotheses belong. We apply the general bounds to bound the performance of some learning algorithms based on empirical risk minimization and stochastic gradient descent.

We consider structure discovery of undirected graphical models from observational data. Inferring likely structures from few examples is a complex task often requiring the formulation of priors and sophisticated inference procedures. Popular methods rely on estimating a penalized maximum likelihood of the precision matrix. However, in these approaches structure recovery is an indirect consequence of the data-fit term, the penalty can be difficult to adapt for domain-specific knowledge, and the inference is computationally demanding. By contrast, it may be easier to generate training samples of data that arise from graphs with the desired structure properties. We propose here to leverage this latter source of information as training data to learn a function, parametrized by a neural network that maps empirical covariance matrices to estimated graph structures. Learning this function brings two benefits: it implicitly models the desired structure or sparsity properties to form suitable priors, and it can be tailored to the specific problem of edge structure discovery, rather than maximizing data likelihood. Applying this framework, we find our learnable graph-discovery method trained on synthetic data generalizes well: identifying relevant edges in both synthetic and real data, completely unknown at training time. We find that on genetics, brain imaging, and simulation data we obtain performance generally superior to analytical methods.

Especially in the community of Digital Humanities, the automated processing of Latin texts has always been a popular research topic. In a variety of computational applications, such as text reuse detection [Franzini et al, 2015], it is desirable to annotate and augment Latin texts with useful morpho-syntactical or lexical information, such as lemmas. In this paper, we will focus on two sequence tagging tasks for medieval Latin: part-of-speech tagging and lemmatization. Given a piece of Latin text, the task of lemmatization involves assigning each word to a single dictionary headword or'lemma': a baseform label (preferably in a normalized orthography) grouping all word tokens which only differ in spelling and/or inflection [Knowles et al, 2004]. The task of lemmatization is closely related to that of part-of-speech (PoS) tagging [Jurafsky et al, 2000], in which each word in a running text should be assigned a tag indicating its part of speech or word class (e.g.

In Bayesian statistics, probabilities are interpreted as degrees of belief. For any set of mutually exclusive and exhaustive events, one expresses the state of knowledge as a probability distribution over that set. The probability of an event then describes the personal confidence that this event will happen or has happened. As a consequence, probabilities are subjective properties reflecting the amount of knowledge an observer has about the events; a different observer might know which event happened and assign different probabilities. If an observer gains information, she updates the probabilities she had assigned before. If the set of possible mutually exclusive and exhaustive events is infinite, it is generally impossible to store all entries of the corresponding probability distribution on a computer or communicate it through a channel with finite bandwidth. One therefore needs to approximate the probability distribution which describes one's belief. Given a limited set X of approximative beliefs q(s) on a quantity s, what is the best belief to approximate the actual belief as expressed by the probability p(s)? In the literature, it is sometimes claimed that the best approximation is given by the q X that minimizes the Kullback-Leibler divergence ("approximation" KL) [1] KL(p, q) () p(s) p(s) ln (1) q(s)

Latent tree models are graphical models defined on trees, in which only a subset of variables is observed. They were first discussed by Judea Pearl as tree-decomposable distributions to generalise star-decomposable distributions such as the latent class model. Latent tree models, or their submodels, are widely used in: phylogenetic analysis, network tomography, computer vision, causal modeling, and data clustering. They also contain other well-known classes of models like hidden Markov models, Brownian motion tree model, the Ising model on a tree, and many popular models used in phylogenetics. We offer here a concise introduction to the theory of latent tree models. We emphasise the role of tree metrics in the structural description of this model class, in designing learning algorithms, and in understanding fundamental limits of what and when can be learned. We present Gaussian and general Markov models as subclasses of latent tree models that admits tractable and rigorous analysis. A leaf of T is a vertex of degree one, an internal vertex is a vertex which is not a leaf, and an inner edge is an edge whose both ends are internal vertices. Given a treeT define a rooted tree as a directed graph obtained from T by picking one of its verticesr and directing all edges away fromr . The vertexr is called the root. Trees will be always leaf-labeled with the labelling set{ 1,...,m}, where m is the number of leaves. An undirected tree is trivalent if each internal vertex has degree precisely three. A rooted tree is a binary rooted tree if each internal vertex has precisely two children. In many applications rooted trees are depicted without using arrows, where direction is made implicit by drawing the root on the top and the leaves on the bottom; see Figure 1(c). Two special types of undirected trees are: a star tree with one internal vertex and a trivalent tree on four leaves called a quartet tree; see Figure 1(a) and (b). A forest is a collection of trees. Forests here are also leaf-labeled with the labelling set is{ 1,...,m}, which means that each tree in this collection is leaf-labeled and the corresponding collection of labelling sets forms a set partition of { 1,...,m}. We define three graph operations on trees (forests). Removing an edge means removing that edge from the edge set. Contracting an edge u v means removingu,v from the vertex set, adding a new vertexw and edges such thatw is adjacent to all vertices which were adjacent tou or v. Suppressing a vertex of degree two means removing that vertex and replacing the two edges incident to that vertex by a single edge. 1 2 3 4 5 1 2 3 4 (a) (b) (c) Figure 1: (a) An undirected star tree with five leaves, (b) a quartet tree, (c) a binary rooted tree.

The Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for specifying LFI models commonly composed of priors, simulators, summaries, distances and other custom operations. These can be implemented in a wide variety of languages. Separating the modelling task from the inference makes it possible to use the same model with any of the available inference methods without modifications. A central method implemented in ELFI is Bayesian Optimization for Likelihood-Free Inference (BOLFI), which has recently been shown to accelerate likelihood-free inference up to several orders of magnitude. ELFI also has an inbuilt support for output data storing for reuse and analysis, and supports parallelization of computation from multiple cores up to a cluster environment. ELFI is designed to be extensible and provides interfaces for widening its functionality. This makes addition of new inference methods to ELFI straightforward and automatically compatible with the inbuilt features.

Streaming Human Learning Outline 3. Andy Petrella Maths scala Apache Spark Spark Notebook Trainer Data Banana Xavier Tordoir Physics Bioinformatics Scala Spark 4. you cannot prove a vague theory is wrong […] Also, if the process of computing the consequences is indefinite, then with a little skill any experimental result can be made to look like the expected consequences. Science with data Surely You're Joking Mr… 5. Modelling without first principle… What is Machine Learning? Overview 2nd law neither... 6. Modelling without first principle… What is Machine Learning? Overview Machine learning you do with a Learning Machine Take that Newton... 7. Modelling without first principle… Modelling dependencies from the data What is Machine Learning? Overview With some "a priori" knowledge 8. What is the problem?

If you sit and think about it for a moment, a made up string of characters is the only thing keeping the world's criminal population from commandeering all your assets. Every online account you have is secured by nothing but a string of characters that anyone can type in. To make matters even worse, you're not supposed to use the same password for multiple online accounts. Of course all these different passwords you're supposed to memorize have to: There is no way you're going to remember 10 passwords that meet each of the above criteria for the 15-20 websites you log into for various reasons. This means we're all supposed to store a big list of passwords somewhere that we reference every time we login to an online account.