We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs). The variables at the bottom level of an HLTM are observed binary variables that represent the presence/absence of words in a document. The variables at other levels are binary latent variables, with those at the lowest latent level representing word co-occurrence patterns and those at higher levels representing co-occurrence of patterns at the level below. Each latent variable gives a soft partition of the documents, and document clusters in the partitions are interpreted as topics. Latent variables at high levels of the hierarchy capture long-range word co-occurrence patterns and hence give thematically more general topics, while those at low levels of the hierarchy capture short-range word co-occurrence patterns and give thematically more specific topics. Unlike LDA-based topic models, HLTMs do not refer to a document generation process and use word variables instead of token variables. They use a tree structure to model the relationships between topics and words, which is conducive to the discovery of meaningful topics and topic hierarchies.

Despite tremendous progress in outlier detection research in recent years, the majority of existing methods are designed only to detect unconditional outliers that correspond to unusual data patterns expressed in the joint space of all data attributes. Such methods are not applicable when we seek to detect conditional outliers that reflect unusual responses associated with a given context or condition. This work focuses on multivariate conditional outlier detection, a special type of the conditional outlier detection problem, where data instances consist of multi-dimensional input (context) and output (responses) pairs. We present a novel outlier detection framework that identifies abnormal input-output associations in data with the help of a decomposable conditional probabilistic model that is learned from all data instances. Since components of this model can vary in their quality, we combine them with the help of weights reflecting their reliability in assessment of outliers. We study two ways of calculating the component weights: global that relies on all data, and local that relies only on instances similar to the target instance. Experimental results on data from various domains demonstrate the ability of our framework to successfully identify multivariate conditional outliers.

Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecond order statistical measure in kernel space, called the kernel mean-p power error (KMPE), including the correntropic loss (CLoss) as a special case. Some basic properties of KMPE are presented. In particular, we apply the KMPE to extreme learning machine (ELM) and principal component analysis (PCA), and develop two robust learning algorithms, namely ELM-KMPE and PCA-KMPE. Experimental results on synthetic and benchmark data show that the developed algorithms can achieve consistently better performance when compared with some existing methods.

In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline. Feature engineering is widely considered as the most time consuming and expert knowledge demanding portion of any ML task. The proposed feature recommendation approach is tested on 3 healthcare datasets: a) PhysioNet Challenge 2016 dataset of phonocardiogram (PCG) signals, b) MIMIC II blood pressure classification dataset of photoplethysmogram (PPG) signals and c) an emotion classification dataset of PPG signals. While the proposed method beats the state of the art techniques for 2nd and 3rd dataset, it reaches 94.38% of the accuracy level of the winner of PhysioNet Challenge 2016. In all cases, the effort to reach a satisfactory performance was drastically less (a few days) than manual feature engineering.

Several methods exist to infer causal networks from massive volumes of observational data. However, almost all existing methods require a considerable length of time series data to capture cause and effect relationships. In contrast, memory-less transition networks or Markov Chain data, which refers to one-step transitions to and from an event, have not been explored for causality inference even though such data is widely available. We find that causal network can be inferred from characteristics of four unique distribution zones around each event. We call this Composition of Transitions and show that cause, effect, and random events exhibit different behavior in their compositions. We applied machine learning models to learn these different behaviors and to infer causality. We name this new method Causality Inference using Composition of Transitions (CICT). To evaluate CICT, we used an administrative inpatient healthcare dataset to set up a network of patients transitions between different diagnoses. We show that CICT is highly accurate in inferring whether the transition between a pair of events is causal or random and performs well in identifying the direction of causality in a bi-directional association.

We consider the problem of online active learning to collect data for regression modeling. Specifically, we consider a decision maker with a limited experimentation budget who must efficiently learn an underlying linear population model. Our main contribution is a novel threshold-based algorithm for selection of most informative observations; we characterize its performance and fundamental lower bounds. We extend the algorithm and its guarantees to sparse linear regression in high-dimensional settings. Simulations suggest the algorithm is remarkably robust: it provides significant benefits over passive random sampling in real-world datasets that exhibit high nonlinearity and high dimensionality --- significantly reducing both the mean and variance of the squared error.

Nick Foti, Ryan Adams, and I just put a paper on the arxiv about improving variational approximations (short version accepted early to AABI2016). We focused on one problematic aspect of variational inference in practice -- that once the optimization problem is solved, the approximation is set and there isn't a straightforward way to improve it, even when we can afford some extra compute time. Markov chain Monte Carlo methods have a simple solution -- run the chain for more steps and the posterior approximation will get better and better. When that pre-specified class of distributions (the variational family) doesn't include the neighborhood around the target distribution, the resulting VI solution will still be sub-optimal, in that there will be a non-zero KL-divergence between the approximation and the target. This will result in biased posterior estimates.

Bayesian Reasoning and Machine Learning by David Barber has a chapter on Approximate Sampling Christophe Andrieu et al. have written an introductory tutorial (pdf) on MCMC methods that covers most of the MCMC algorithms Dr. Daphne Koller offers an online course on Coursera, Probabilistic Graphical Models, which also covers the Gibbs Sampler and the Metropolis-Hastings Algorithm Dr. A. Taylan Cemgil has prepared very useful lecture notes (pdf) for his Monte Carlo methods course

In this post you discovered how to persist your machine learning algorithms in Python with scikit-learn.

Machine Learning is the foundation for today's insights on customer, products, costs and revenues which learns from the data provided to its algorithms. Some of the most common examples of machine learning are Netflix's algorithms to give movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend products based on other customers bought before. Decision Trees: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Fastest way to identify most significant variables and relation between two or more variables.