Learning Markov networks with context-specific independences

Learning the Markov network structure from data is a problem that has received considerable attention in machine learning, and in many other application fields. This work focuses on a particular approach for this purpose called independence-based learning. Such approach guarantees the learning of the correct structure efficiently, whenever data is sufficient for representing the underlying distribution. However, an important issue of such approach is that the learned structures are encoded in an undirected graph. The problem with graphs is that they cannot encode some types of independence relations, such as the context-specific independences. They are a particular case of conditional independences that is true only for a certain assignment of its conditioning set, in contrast to conditional independences that must hold for all its assignments. In this work we present CSPC, an independence-based algorithm for learning structures that encode context-specific independences, and encoding them in a log-linear model, instead of a graph. The central idea of CSPC is combining the theoretical guarantees provided by the independence-based approach with the benefits of representing complex structures by using features in a log-linear model. We present experiments in a synthetic case, showing that CSPC is more accurate than the state-of-the-art IB algorithms when the underlying distribution contains CSIs.Comment: 8 pages, 6 figure

Aprendizaje de estructuras de independencia de modelos probabilísticos gráficos

Nuestra investigación se enmarca en el problema del aprendizaje, a partir de datos, de estructuras de independencia de modelos probabilísticos gráficos. Es de especial interés el aprendizaje automatizado de estos modelos a partir de datos, debido principalmente a la presencia cada vez más ubicua de datos digitales. El campo del aprendizaje de máquinas en general, y en particular los miembros de nuestro laboratorio, se han concentrado en el aprendizaje del grafo que representa la estructura de independencias de estos modelos. Durante su tésis doctoral el Dr Bromberg (Bromberg 2007) se ha concentrado en el diseño de algoritmos de aprendizaje de estructuras que utilizan un enfoque basado en independencias (Spirtes et. al. 2000), en contraste con los algoritmos basados en puntaje (Lam and Bacchus 1994, Heckerman 1995). Estos últimos recurren a técnicas para aprendizaje de modelos mas establecidas en la estadística como ser por ejemplo la maximización de la verosimilitud (probabilidad del los datos dado el modelo). El enfoque basado en independencias, en cambio, utiliza un enfoque mas directo para aprender la estructura de independencias del modelo, realizando tests estadísticos de independencia entre las variables aleatorias del sistema. Durante su estadía en Iowa State University, y durante el pasado año ya en UTN-FRM, el Dr. Bromberg ha contribuido con varios algoritmos para el aprendizaje de estructuras de modelos Markovianos con el objetivo de reducir la cantidad de tests estadísticos necesarios durante su ejecución. Recientemente el laboratorio se ha enfocado en un problema más exigente y más importante, el diseño de algoritmos que ante la misma entrada de datos, produzcan modelos de mejor calidad. Estos algoritmos son aplicables tanto a redes Markovianas como Bayesianas.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Aprendizaje de estructuras de independencia de modelos probabilísticos gráficos

Nuestra investigación se enmarca en el problema del aprendizaje, a partir de datos, de estructuras de independencia de modelos probabilísticos gráficos. Es de especial interés el aprendizaje automatizado de estos modelos a partir de datos, debido principalmente a la presencia cada vez más ubicua de datos digitales. El campo del aprendizaje de máquinas en general, y en particular los miembros de nuestro laboratorio, se han concentrado en el aprendizaje del grafo que representa la estructura de independencias de estos modelos. Durante su tésis doctoral el Dr Bromberg (Bromberg 2007) se ha concentrado en el diseño de algoritmos de aprendizaje de estructuras que utilizan un enfoque basado en independencias (Spirtes et. al. 2000), en contraste con los algoritmos basados en puntaje (Lam and Bacchus 1994, Heckerman 1995). Estos últimos recurren a técnicas para aprendizaje de modelos mas establecidas en la estadística como ser por ejemplo la maximización de la verosimilitud (probabilidad del los datos dado el modelo). El enfoque basado en independencias, en cambio, utiliza un enfoque mas directo para aprender la estructura de independencias del modelo, realizando tests estadísticos de independencia entre las variables aleatorias del sistema. Durante su estadía en Iowa State University, y durante el pasado año ya en UTN-FRM, el Dr. Bromberg ha contribuido con varios algoritmos para el aprendizaje de estructuras de modelos Markovianos con el objetivo de reducir la cantidad de tests estadísticos necesarios durante su ejecución. Recientemente el laboratorio se ha enfocado en un problema más exigente y más importante, el diseño de algoritmos que ante la misma entrada de datos, produzcan modelos de mejor calidad. Estos algoritmos son aplicables tanto a redes Markovianas como Bayesianas.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Speeding up the execution of a large number of statistical tests of independence

A massive amount of conditional independence tests on data must be performed in the problem of learning the structure of probabilistic graphical models when using the independence-based approach. An intermediate step in the computation of independence tests is the construction of contingency tables from the data. In this work we present an intelligent cache of contingency tables that allows the tables stored to be reused not only for the same test, in the not uncommon case that the test must be performed again, but for an exponential number of other tests, all those involving a subset of the variables of the test stored. In practice, however, not so many tests actually reuse the tables stored. We show results when testing the cache with IBMAP-HC, a recently proposed algorithm for learning the structure of Markov networks, a.k.a. undirected graphical models. The experiments show that in all cases, above 95% of the running time spent by IBMAP-HC in reading data is saved by the cache. The savings in running time for IBMAP-HC were up to 80% for datasets above 40,000 datapoints.Sociedad Argentina de Informática e Investigación Operativ

The IBMAP approach for Markov networks structure learning

In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum

A20 regulates lymphocyte adhesion in murine neuroinflammation by restricting endothelial ICOSL expression in the CNS.

A20 is a ubiquitin-modifying protein that negatively regulates NF-κB signaling. Mutations in A20/TNFAIP3 are associated with a variety of autoimmune diseases, including multiple sclerosis (MS). We found that deletion of A20 in central nervous system (CNS) endothelial cells (ECs) enhances experimental autoimmune encephalomyelitis (EAE), a mouse model of MS. A20∆CNS-EC mice showed increased numbers of CNS-infiltrating immune cells during neuroinflammation and in the steady state. While the integrity of the blood-brain barrier (BBB) was not impaired, we observed a strong activation of CNS-ECs in these mice, with dramatically increased levels of the adhesion molecules ICAM-1 and VCAM-1. We discovered ICOSL as adhesion molecule expressed by A20-deficient CNS-ECs. Silencing of ICOSL in CNS microvascular ECs partly reversed the phenotype of A20∆CNS-EC mice without reaching statistical significance and delayed the onset of EAE symptoms in wildtype mice. In addition, blocking of ICOSL on primary mouse brain microvascular endothelial cells (pMBMECs) impaired the adhesion of T cells in vitro. Taken together, we here propose that CNS EC-ICOSL contributes to the firm adhesion of T cells to the BBB, promoting their entry into the CNS and eventually driving neuroinflammation

A survey on independence-based Markov networks learning

This work reports the most relevant technical aspects in the problem of learning the \emph{Markov network structure} from data. Such problem has become increasingly important in machine learning, and many other application fields of machine learning. Markov networks, together with Bayesian networks, are probabilistic graphical models, a widely used formalism for handling probability distributions in intelligent systems. Learning graphical models from data have been extensively applied for the case of Bayesian networks, but for Markov networks learning it is not tractable in practice. However, this situation is changing with time, given the exponential growth of computers capacity, the plethora of available digital data, and the researching on new learning technologies. This work stresses on a technology called independence-based learning, which allows the learning of the independence structure of those networks from data in an efficient and sound manner, whenever the dataset is sufficiently large, and data is a representative sampling of the target distribution. In the analysis of such technology, this work surveys the current state-of-the-art algorithms for learning Markov networks structure, discussing its current limitations, and proposing a series of open problems where future works may produce some advances in the area in terms of quality and efficiency. The paper concludes by opening a discussion about how to develop a general formalism for improving the quality of the structures learned, when data is scarce.Comment: 35 pages, 1 figur

EuReCa ONE—27 Nations, ONE Europe, ONE Registry A prospective one month analysis of out-of-hospital cardiac arrest outcomes in 27 countries in Europe

AbstractIntroductionThe aim of the EuReCa ONE study was to determine the incidence, process, and outcome for out of hospital cardiac arrest (OHCA) throughout Europe.MethodsThis was an international, prospective, multi-centre one-month study. Patients who suffered an OHCA during October 2014 who were attended and/or treated by an Emergency Medical Service (EMS) were eligible for inclusion in the study. Data were extracted from national, regional or local registries.ResultsData on 10,682 confirmed OHCAs from 248 regions in 27 countries, covering an estimated population of 174 million. In 7146 (66%) cases, CPR was started by a bystander or by the EMS. The incidence of CPR attempts ranged from 19.0 to 104.0 per 100,000 population per year. 1735 had ROSC on arrival at hospital (25.2%), Overall, 662/6414 (10.3%) in all cases with CPR attempted survived for at least 30 days or to hospital discharge.ConclusionThe results of EuReCa ONE highlight that OHCA is still a major public health problem accounting for a substantial number of deaths in Europe.EuReCa ONE very clearly demonstrates marked differences in the processes for data collection and reported outcomes following OHCA all over Europe. Using these data and analyses, different countries, regions, systems, and concepts can benchmark themselves and may learn from each other to further improve survival following one of our major health care events