We are living in a digital era where Customer is the king. Many businesses have capitulated to this new realm and have started interacting with customers dynamically. Today the customers are free to navigate a merchant (eCommerce) website any way they fancy. Also the merchant can display content and place offers dynamically based on how a given customer interacts with his website. To add to the complexity purchase decisions are not necessarily made on the first visit itself.

We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country $i$ took action $a$ toward country $j$ at time $t$." BPTD discovers overlapping country--community memberships, including the number of latent communities. In addition, it discovers directed community--community interaction networks that are specific to "topics" of action types and temporal "regimes." We show that BPTD yields an efficient MCMC inference algorithm and achieves better predictive performance than related models. We also demonstrate that it discovers interpretable latent structure that agrees with our knowledge of international relations.

Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm. We approach this problem from a reinforcement learning perspective and represent any particular optimization algorithm as a policy. We learn an optimization algorithm using guided policy search and demonstrate that the resulting algorithm outperforms existing hand-engineered algorithms in terms of convergence speed and/or the final objective value.

We consider the problem of learning multi-ridge functions of the form f(x) = g(Ax) from point evaluations of f. We assume that the function f is defined on an l_2-ball in R^d, g is twice continuously differentiable almost everywhere, and A \in R^{k \times d} is a rank k matrix, where k << d. We propose a randomized, polynomial-complexity sampling scheme for estimating such functions. Our theoretical developments leverage recent techniques from low rank matrix recovery, which enables us to derive a polynomial time estimator of the function f along with uniform approximation guarantees. We prove that our scheme can also be applied for learning functions of the form: f(x) = \sum_{i=1}^{k} g_i(a_i^T x), provided f satisfies certain smoothness conditions in a neighborhood around the origin. We also characterize the noise robustness of the scheme. Finally, we present numerical examples to illustrate the theoretical bounds in action.

I've been re-reading Frank Harrell's Regression Modelling Strategies, a must read for anyone who ever fits a regression model, although be prepared - depending on your background, you might get 30 pages in and suddenly become convinced you've been doing nearly everything wrong before, which can be disturbing. I wanted to evaluate three simple modelling strategies in dealing with data with many variables. Using data with 54 variables on 1,785 area units from New Zealand's 2013 census, I'm looking to predict median income on the basis of the other 53 variables. The features are all continuous and are variables like "mean number of bedrooms", "proportion of individuals with no religion" and "proportion of individuals who are smokers". None of these is exactly what I would use for real, but they serve the purpose of setting up a competition of strategies that I can test with a variety of model validation techniques.

Specifically, using PL/Python, one can bring in countless Python libraries to process data close to the database. Here I will talk about my efforts to bring in the functionality of PySAL, a spatial analytics library written in Python and developed largely by Serge Rey, et al. at Arizona State University. PySAL makes available robust exploratory spatial data analysis related to spatial cluster and outlier detection, hotspot detection, spatial regression, and much more. Besides the wrappers we wrote for PySAL, we have written classes for bringing in machine learning methods such as random forest, linear regression, support vector machines, and neural networks from scikit-learn and Tensorflow. This talk will specifically cover the challenges we encountered programming in the PL/Python environment, collaborations with some of the PySAL developers, and the power of having spatial statistics and machine learning capabilities baked right into a cloud database.

To expand: according to my naive understanding, boosted trees are basically an ensemble of decision trees which are fit sequentially so that each new tree makes up for the errors of the previously existing set of trees. The model is "boosted" by focusing new additions on correcting the residual errors of the last version of the model. The "gradient" comes in afterward as the parameters of the tree ensemble are optimized to minimize the error of the whole "base learner". I think of this as fine tuning of the boosted tree ensemble using a gradient-based optimization.

In this post we take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so many algorithms available and it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit. In this post I want to give you two ways to think about and categorize the algorithms you may come across in the field. Both approaches are useful, but we will focus in on the grouping of algorithms by similarity and go on a tour of a variety of different algorithm types.

In the gradient descent method of optimization, a hypothesis function, h_\boldsymbol{\theta}(x), is fitted to a data set, (x {(i)}, y {(i)}) ( i 1,2,\cdots,m) by minimizing an associated cost function, J(\boldsymbol{\theta}) in terms of the parameters \boldsymbol\theta \theta_0, \theta_1, \cdots . The cost function describes how closely the hypothesis fits the data for a given choice of \boldsymbol \theta . For example, one might wish to fit a given data set to a straight line, h_\boldsymbol{\theta}(x) \theta_0 \theta_1 x. To simplify things, consider fitting a data set to a straight line through the origin: h_\theta(x) \theta_1 x . In this one-dimensional problem, we can plot a simple graph for J(\theta_1) and follow the iterative procedure which trys to converge on its minimum.

To begin with, you need to provide us more information regarding what kind of data you have, what your objectives and research questions were so we can provide you with relevant help so as not to speculate. However, a general principle which I have used many often successfully is to conduct univariate regression on the combined effect of each categorical variable and then used follow on with multiple regression. If the combined effect of that categorical variable is not significant, there is no need to declare the classes for such such variables in the multiple regression model or if some of the classes are similar in nature, you could collapse then into one class and then test their combined effect again by repeating the process above. You will do this for all the categorical variables in your data set. Yes, you can use linear regression to achieve this but having 100 classes for one categorical variable, I am afraid that you will be dealing with so many degrees of freedom which might have some serious effects on the optimality of your fitted model and its predictive power so I will suggest you collapse the classes to fewer if that is possible, bearing in mind your research questions and objectives.