How to Knit Your Own Markov Blanket

Hohwy (Hohwy 2016, Hohwy 2017) argues there is a tension between the free energy principle and leading depictions of mind as embodied, enactive, and extended (so-called ‘EEE1 cognition’). The tension is traced to the importance, in free energy formulations, of a conception of mind and agency that depends upon the presence of a ‘Markov blanket’ demarcating the agent from the surrounding world. In what follows I show that the Markov blanket considerations do not, in fact, lead to the kinds of tension that Hohwy depicts. On the contrary, they actively favour the EEE story. This is because the Markov property, as exemplified in biological agents, picks out neither a unique nor a stationary boundary. It is this multiplicity and mutability– rather than the absence of agent-environment boundaries as such - that EEE cognition celebrates

The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences

Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitatively describes independences in the distribution, which can be exploited to factorize the distribution into a set of compact functions. A key application for learning structures from data is to automatically discover knowledge. In practice, structure learning algorithms focused on "knowledge discovery" present a limitation: they use a coarse-grained representation of the structure. As a result, this representation cannot describe context-specific independences. Very recently, an algorithm called CSPC was designed to overcome this limitation, but it has a high computational complexity. This work tries to mitigate this downside presenting CSGS, an algorithm that uses the Grow-Shrink strategy for reducing unnecessary computations. On an empirical evaluation, the structures learned by CSGS achieve competitive accuracies and lower computational complexity with respect to those obtained by CSPC.Comment: 12 pages, and 8 figures. This works was presented in IBERAMIA 201

Massively-Parallel Feature Selection for Big Data

We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size). To tackle the challenges of Big Data FS PFBP partitions the data matrix both in terms of rows (samples, training examples) as well as columns (features). By employing the concepts of $p$-values of conditional independence tests and meta-analysis techniques PFBP manages to rely only on computations local to a partition while minimizing communication costs. Then, it employs powerful and safe (asymptotically sound) heuristics to make early, approximate decisions, such as Early Dropping of features from consideration in subsequent iterations, Early Stopping of consideration of features within the same iteration, or Early Return of the winner in each iteration. PFBP provides asymptotic guarantees of optimality for data distributions faithfully representable by a causal network (Bayesian network or maximal ancestral graph). Our empirical analysis confirms a super-linear speedup of the algorithm with increasing sample size, linear scalability with respect to the number of features and processing cores, while dominating other competitive algorithms in its class

A Semi-Supervised Feature Engineering Method for Effective Outlier Detection in Mixed Attribute Data Sets

Outlier detection is one of the crucial tasks in data mining which can lead to the finding of valuable and meaningful information within the data. An outlier is a data point that is notably dissimilar from other data points in the data set. As such, the methods for outlier detection play an important role in identifying and removing the outliers, thereby increasing the performance and accuracy of the prediction systems. Outlier detection is used in many areas like financial fraud detection, disease prediction, and network intrusion detection. Traditional outlier detection methods are founded on the use of different distance measures to estimate the similarity between the points and are confined to data sets that are purely continuous or categorical. These methods, though effective, lack in elucidating the relationship between outliers and known clusters/classes in the data set. We refer to this relationship as the context for any reported outlier. Alternate outlier detection methods establish the context of a reported outlier using underlying contextual beliefs of the data. Contextual beliefs are the established relationships between the attributes of the data set. Various studies have been recently conducted where they explore the contextual beliefs to determine outlier behavior. However, these methods do not scale in the situations where the data points and their respective contexts are sparse. Thus, the outliers reported by these methods tend to lose meaning. Another limitation of these methods is that they assume all features are equally important and do not consider nor determine subspaces among the features for identifying the outliers. Furthermore, determining subspaces is computationally exacerbated, as the number of possible subspaces increases with increasing dimensionality. This makes searching through all the possible subspaces impractical. In this thesis, we propose a Hybrid Bayesian Network approach to capture the underlying contextual beliefs to detect meaningful outliers in mixed attribute data sets. Hybrid Bayesian Networks utilize their probability distributions to encode the information of the data and outliers are those points which violate this information. To deal with the sparse contexts, we use an angle-based similarity method which is then combined with the joint probability distributions of the Hybrid Bayesian Network in a robust manner. With regards to the subspace selection, we employ a feature engineering method that consists of two-stage feature selection using Maximal Information Coefficient and Markov blankets of Hybrid Bayesian Networks to select highly correlated feature subspaces. This proposed method was tested on a real world medical record data set. The results indicate that the algorithm was able to identify meaningful outliers successfully. Moreover, we compare the performance of our algorithm with the existing baseline outlier detection algorithms. We also present a detailed analysis of the reported outliers using our method and demonstrate its efficiency when handling data points with sparse contexts

Marginal Pseudo-Likelihood Learning of Discrete Markov Network Structures

Markov networks are a popular tool for modeling multivariate distributions over a set of discrete variables. The core of the Markov network representation is an undirected graph which elegantly captures the dependence structure over the variables. Traditionally, the Bayesian approach of learning the graph structure from data has been done under the assumption of chordality since non-chordal graphs are difficult to evaluate for likelihood-based scores. Recently, there has been a surge of interest towards the use of regularized pseudo-likelihood methods as such approaches can avoid the assumption of chordality. Many of the currently available methods necessitate the use of a tuning parameter to adapt the level of regularization for a particular dataset. Here we introduce the marginal pseudo-likelihood which has a built-in regularization through marginalization over the graph-specific nuisance parameters. We prove consistency of the resulting graph estimator via comparison with the pseudo-Bayesian information criterion. To identify high-scoring graph structures in a high-dimensional setting we design a two-step algorithm that exploits the decomposable structure of the score. Using synthetic and existing benchmark networks, the marginal pseudo-likelihood method is shown to perform favorably against recent popular structure learning methods.Peer reviewe

Where there is life there is mind: In support of a strong life-mind continuity thesis

This paper considers questions about continuity and discontinuity between life and mind. It begins by examining such questions from the perspective of the free energy principle (FEP). The FEP is becoming increasingly influential in neuroscience and cognitive science. It says that organisms act to maintain themselves in their expected biological and cognitive states, and that they can do so only by minimizing their free energy given that the long-term average of free energy is entropy. The paper then argues that there is no singular interpretation of the FEP for thinking about the relation between life and mind. Some FEP formulations express what we call an independence view of life and mind. One independence view is a cognitivist view of the FEP. It turns on information processing with semantic content, thus restricting the range of systems capable of exhibiting mentality. Other independence views exemplify what we call an overly generous non-cognitivist view of the FEP, and these appear to go in the opposite direction. That is, they imply that mentality is nearly everywhere. The paper proceeds to argue that non-cognitivist FEP, and its implications for thinking about the relation between life and mind, can be usefully constrained by key ideas in recent enactive approaches to cognitive science. We conclude that the most compelling account of the relationship between life and mind treats them as strongly continuous, and that this continuity is based on particular concepts of life (autopoiesis and adaptivity) and mind (basic and non-semantic)