In the Pioneer 100 (P100) Wellness Project (Price and others, 2017), multiple types of data are collected on a single set of healthy participants at multiple timepoints in order to characterize and optimize wellness. One way to do this is to identify clusters, or subgroups, among the participants, and then to tailor personalized health recommendations to each subgroup. It is tempting to cluster the participants using all of the data types and timepoints, in order to fully exploit the available information. However, clustering the participants based on multiple data views implicitly assumes that a single underlying clustering of the participants is shared across all data views. If this assumption does not hold, then clustering the participants using multiple data views may lead to spurious results. In this paper, we seek to evaluate the assumption that there is some underlying relationship among the clusterings from the different data views, by asking the question: are the clusters within each data view dependent or independent? We develop a new test for answering this question, which we then apply to clinical, proteomic, and metabolomic data, across two distinct timepoints, from the P100 study. We find that while the subgroups of the participants defined with respect to any single data type seem to be dependent across time, the clustering among the participants based on one data type (e.g. proteomic data) appears not to be associated with the clustering based on another data type (e.g. clinical data).

Prediction accuracy and model explainability are the two most important objectives when developing machine learning algorithms to solve real-world problems. The neural networks are known to possess good prediction performance, but lack of sufficient model explainability. In this paper, we propose to enhance the explainability of neural networks through the following architecture constraints: a) sparse additive subnetworks; b) orthogonal projection pursuit; and c) smooth function approximation. It leads to a sparse, orthogonal and smooth explainable neural network (SOSxNN). The multiple parameters in the SOSxNN model are simultaneously estimated by a modified mini-batch gradient descent algorithm based on the backpropagation technique for calculating the derivatives and the Cayley transform for preserving the projection orthogonality. The hyperparameters controlling the sparse and smooth constraints are optimized by the grid search. Through simulation studies, we compare the SOSxNN method to several benchmark methods including least absolute shrinkage and selection operator, support vector machine, random forest, and multi-layer perceptron. It is shown that proposed model keeps the flexibility of pursuing prediction accuracy while attaining the improved interpretability, which can be therefore used as a promising surrogate model for complex model approximation. Finally, the real data example from the Lending Club is employed as a showcase of the SOSxNN application.

Diffusion reach probability between two nodes on a network is defined as the probability of a cascade originating from one node reaching to another node. An infinite number of cascades would enable calculation of true diffusion reach probabilities between any two nodes. However, there exists only a finite number of cascades and one usually has access only to a small portion of all available cascades. In this work, we addressed the problem of estimating diffusion reach probabilities given only a limited number of cascades and partial information about underlying network structure. Our proposed strategy employs node representation learning to generate and feed node embeddings into machine learning algorithms to create models that predict diffusion reach probabilities. We provide experimental analysis using synthetically generated cascades on two real-world social networks. Results show that proposed method is superior to using values calculated from available cascades when the portion of cascades is small.

The development of the perceptron was a big step towards the goal of creating useful connectionist networks capable of learning complex relations between inputs and outputs. In the late 1950's, the connectionist community understood that what was needed for further development of connectionist models was a mathematically-derived (and thus potentially more flexible and powerful) rule for learning. By early 1960's, the Delta Rule [also known as the Widrow & Hoff Learning rule or the Least Mean Square (LMS) rule] was invented by Widrow and Hoff. This rule is similar to the perceptron learning rule by McClelland & Rumelhart, 1988, but is also characterized by a mathematical utility and elegance missing in the perceptron and other early learning rules. The Delta Rule uses the difference between target activation (i.e., target output values) and obtained activation to drive learning.

I recently came across a new [to me] approach, gradient boosting machines (specifically XGBoost), in the book Deep Learning with Python by François Chollet. Chollet mentions that XGBoost is the one shallow learning technique that a successful applied machine learner should be familiar with today, so I took his word for it and dove in to learn more. I mostly wanted to write this article because I thought that others with some knowledge of machine learning also may have missed this topic as I did. I am by no means an expert on the topic and to be honest had trouble understanding some of the mechanics, however, I hope this article is a great primer to your exploration on the subject (list of great resources at the bottom too)! So what is XGBoost and where does it fit in the world of ML? Gradient Boosting Machines fit into a category of ML called Ensemble Learning, which is a branch of ML methods that train and predict with many models at once to produce a single superior output.

Machine learning is a wildly popular field of technology that is being used by data scientists around the globe. Mastering machine learning can be achieved via many avenues of study, but one arguably necessary ingredient to success is a fundamental understanding of the mathematics behind the algorithms. Some data scientists-in-training often try to take a shortcut and bypass the math, but that route is shortsighted. In order to get the most out of machine learning, you really need that important perspective for what the algorithm is really doing behind the scenes. This perspective is only available with the math.

In person attributes recognition, we describe a person in terms of their appearance. Typically, this includes a wide range of traits including age, gender, clothing, and footwear. Although this could be used in a wide variety of scenarios, it generally is applied to video surveillance, where attribute recognition is impacted by low resolution, and other issues such as variable pose, occlusion and shadow. Recent approaches have used deep convolutional neural networks (CNNs) to improve the accuracy in person attribute recognition. However, many of these networks are relatively shallow and it is unclear to what extent they use contextual cues to improve classification accuracy. In this paper, we propose deeper methods for person attribute recognition. Interpreting the reasons behind the classification is highly important, as it can provide insight into how the classifier is making decisions. Interpretation suggests that deeper networks generally take more contextual information into consideration, which helps improve classification accuracy and generalizability. We present experimental analysis and results for whole body attributes using the PA-100K and PETA datasets and facial attributes using the CelebA dataset.

Dataset pruning is the process of removing sub-optimal tuples from a dataset to improve the learning of a machine learning model. In this paper, we compared the performance of different algorithms, first on an unpruned dataset and then on an iteratively pruned dataset. The goal was to understand whether an algorithm (say A) on an unpruned dataset performs better than another algorithm (say B), will algorithm B perform better on the pruned data or vice-versa. The dataset chosen for our analysis is a subset of the largest movie ratings database publicly available on the internet, IMDb [1]. The learning objective of the model was to predict the categorical rating of a movie among 5 bins: poor, average, good, very good, excellent. The results indicated that an algorithm that performed better on an unpruned dataset also performed better on a pruned dataset.

We consider non-parametric estimation and inference of conditional moment models in high dimensions. We show that even when the dimension $D$ of the conditioning variable is larger than the sample size $n$, estimation and inference is feasible as long as the distribution of the conditioning variable has small intrinsic dimension $d$, as measured by the doubling dimension. Our estimation is based on a sub-sampled ensemble of the $k$-nearest neighbors $Z$-estimator. We show that if the intrinsic dimension of the co-variate distribution is equal to $d$, then the finite sample estimation error of our estimator is of order $n^{-1/(d+2)}$ and our estimate is $n^{1/(d+2)}$-asymptotically normal, irrespective of $D$. We discuss extensions and applications to heterogeneous treatment effect estimation.

Imitation learning is a paradigm that learns from expert demonstration to perform a task. The most straightforward approach of imitation learning is behavioral cloning (Pomerleau, 1991), which learns from expert trajectories to predict the expert action at any state. Despite its simplicity, behavioral cloning ignores the accumulation of prediction error over time. Consequently, although the learned policy closely resembles the expert policy at a given point in time, their trajectories may diverge in the long term. To remedy the issue of error accumulation, inverse reinforcement learning(Russell, 1998; Ng and Russell, 2000; Abbeel and Ng, 2004; Ratliff et al., 2006; Ziebart et al., 2008; Ho and Ermon, 2016) jointly learns a reward function and the corresponding optimal policy, such that the expected cumulative