We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, termed variational boosting, iteratively refines an existing variational approximation by solving a sequence of optimization problems, allowing the practitioner to trade computation time for accuracy. We show how to expand the variational approximating class by incorporating additional covariance structure and by introducing new components to form a mixture. We apply variational boosting to synthetic and real statistical models, and show that resulting posterior inferences compare favorably to existing posterior approximation algorithms in both accuracy and efficiency.

Are you into Machine Learning OR are you "just" a Statistician? Have you been asked this question yet? If you are in a career or looking to get into one that has anything to do with deriving insights out of data, you probably know what I am talking about. The year 2016 has seen over three dozen machine learning startups being acquired by tech giants; another several dozen machine learning startups raked up a aggregate funding to the tune of $4 Billion worldwide. Is it a blip or a bubble?

Indeed, assessment may be better than compared with a conventional test. Griffin's research has found that the tasks on his platform do not exhibit the between nation bias (or Differential Item Functioning) that questions on the standardised, international PISA assessment purportedly suffer from (Kreiner & Christensen, 2014). They are also robust to differences in background language (Vista, Care and Griffin, 2014). The fact that assessment takes a back seat here begs the following question. Of what real worth is the psychometric modelling in the background?

Machine Learning is getting a lot more air time these days but are we actually sure what it is? It gives computers the ability to learn without being explicitly programmed" (Arthur Samuel, 1959). This is an old quote but it has held the test of time. But,how can computers "learn" – have we really reached the age of artificial intelligence where they will take over the world and make humans redundant? Let's explore the core of the definition: the ability to learn What this really means is there are a set of algorithms that, rather than simply following a static set of program instructions, they can make data driven predictions, or decisions through building a model. Supervised learning – The computer is presented with example inputs (training data) and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. The "easiest" example of supervised learning is a decision tree – this uses a tree-like graph or model of decisions and ...

We describe the 1st place winning approach for the CIKM Cup 2016 Challenge. In this paper, we provide an approach to reasonably identify same users across multiple devices based on browsing logs. Our approach regards a candidate ranking problem as pairwise classification and utilizes an unsupervised neural feature ensemble approach to learn latent features of users. Combined with traditional hand crafted features, each user pair feature is fed into a supervised classifier in order to perform pairwise classification. Lastly, we propose supervised and unsupervised inference techniques.

A fresh scrape from Glassdoor gives us a good idea about what applicants are asked during a data scientist interview at some of the top companies. Unfortunately for us, almost every company has their interviewees sign NDAs. Since Glassdoor allows anonymity, a few brave souls have given us some fantastic examples of what they were asked during the interview process at top companies like Facebook, Google, and Microsoft. If you find yourself unable to answer some of the questions below, consider checking out a course or a book on the subject. If you'd like to share your answer(s) to any of the questions, leave a comment and I'll add the top ones to the post.

"Data Scientist is the sexiest job of the 21st century" So, what exactly is data science and why all the hype around data scientists. Frankly speaking, multiple job descriptions and explanations of the same role make it harder for businesses to clearly understand what a data scientist is and does. This complicates the ROI business leaders expect when investing in them. To me, data Science involves mining actionable and sensible insights from multiple data formats by applying mathematics, statistics, machine learning, etc. Data scientists typically analyze data sets, or data depositories that are maintained within an organization and/or they analyze data scraped from publicly available sources.

This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

Gaussian processes are used as flexible models for these functions and estimates are calculated directly from dense data sets using Gaussian process regression. We also develop an approximate expectation maximization algorithm to deal with the unobserved, latent dynamics between sparse observations. The posterior over states is approximated by a piecewise linearized process of the Ornstein-Uhlenbeck type and the maximum a posteriori estimation of the drift is facilitated by a sparse Gaussian process approximation. I. INTRODUCTION Dynamical systems in the physical world evolve in continuous time and often the (noisy) dynamics is described naturally in terms of (stochastic) differential equations [1]. However, due to missing information and/or the complexity of a system it may be difficult to derive such a model from first principles. Instead, the goal often is to fit it to observations of the state at discrete points in time [2]. So far most inference approaches for these systems have dealt with the estimation of parameters contained in the drift function (e.g. Assumptions for the stochastic part were often simple: additive noise with the diffusion constant as the only parameter to estimate. But as both drift and diffusion can be nonlinear functions of the state vector, a nonparametric estimation would be a natural generalization, when a large number of data points is available. Previous nonparametric approaches were based on solving the adjoint Fokker-Planck equation [5] and on kernel estimators [6] and are effectively restricted to one-dimensional models. An alternative would be a Bayesian nonparametric approach, where prior knowledge on the unknown functions--such as smoothness, variability, or periodicity--can be encoded in a probability distribution. A recent result by [7, 8] presented an important step in this direction. The authors have shown that Gaussian processes (GPs) provide a natural family of prior probability measures over drift functions. If a path of the stochastic dynamics is observed densely, the posterior process over the drift is also a GP. Unfortunately, this simplicity is lost, when observations are not dense, but separated by larger time intervals. In [7] the case of sparse observations has been treated by a Monte Carlo approach, which alternates between sampling complete diffusion paths of the stochastic differential equation (SDE) and sampling from GP for the drift given a philipp.batz@tu-berlin.de

Being an unsupervised machine learning and data mining technique, biclustering and its multimodal extensions are becoming popular tools for analysing object-attribute data in different domains. Apart from conventional clustering techniques, biclustering is searching for homogeneous groups of objects while keeping their common description, e.g., in binary setting, their shared attributes. In bioinformatics, biclustering is used to find genes, which are active in a subset of situations, thus being candidates for biomarkers. However, the authors of those biclustering techniques that are popular in gene expression analysis, may overlook the existing methods. For instance, BiMax algorithm is aimed at finding biclusters, which are well-known for decades as formal concepts. Moreover, even if bioinformatics classify the biclustering methods according to reasonable domain-driven criteria, their classification taxonomies may be different from survey to survey and not full as well. So, in this paper we propose to use concept lattices as a tool for taxonomy building (in the biclustering domain) and attribute exploration as means for cross-domain taxonomy completion.