Scientists affiliated with the University of Melbourne and Synchron, Inc. published earlier this week in the Journal of NeuroInterventional Surgery the first-in-human study of Stentrode, a wireless neuroprosthesis that uses machine learning and a stent. What makes the Stentrode technology unique is that it is a stent that records brain activity inside a blood vessel in the brain. It is implanted through the jugular vein so there is no need for open brain surgery. The technology platform originated from the University of Melbourne, in a collaborative effort with the Royal Melbourne Hospital, the Florey Institute of Neuroscience and Mental Health, Monash University, and Synchron, Inc.. A brain-computer interface (BCI) enables two-way communications between the biological brain and a machine.

A recent analysis in Health Services Research and Managerial Epidemiology suggests that machine learning can play a role in helping predict readmissions for atrial fibrillation (AFib). The authors used data from the 2013 Nationwide Readmissions Database on AFib, aiming to create risk prediction models and ultimately predict 90-day hospital readmission rates. The researchers employed multiple machine learning methods (k-Nearest Neighbors, Decision Tree, and Support Vector Machine) to determine variable importance. The average patient age was 64.9 years, with 62% of patients being male. The primary outcome of interest was 90-day hospital readmissions status.

Data science is a very broad multi-disciplinary field that includes several subdivisions such as data visualization, machine learning, and artificial intelligence. Due to the broadness of the field and because data science is constantly changing due to technological innovations and the development of new algorithms, a successful data scientist has to maintain a big and updated toolbox at all times. Keep in mind that as a data scientist, you can only perform tasks that you have the right tools for. This article will discuss several tools that one can include in their data science toolbox. Knowledge-based tools can be grouped into three main categories based on the level of data science tasks involved: level 1 (basic level); level 2 (intermediate level); and level 3 (advanced level). Basic tools are tools that would enable one to perform level 1 tasks.

K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. Typically, unsupervised algorithms make inferences from datasets using only input vectors without referring to known, or labelled, outcomes. AndreyBu, who has more than 5 years of machine learning experience and currently teaches people his skills, says that "the objective of K-means is simple: group similar data points together and discover underlying patterns. To achieve this objective, K-means looks for a fixed number (k) of clusters in a dataset." A cluster refers to a collection of data points aggregated together because of certain similarities. You'll define a target number k, which refers to the number of centroids you need in the dataset.

Machine learning algorithms, like logistic regression and support vector machines, are designed for two-class (binary) classification problems. As such, these algorithms must either be modified for multi-class (more than two) classification problems or not used at all. The Error-Correcting Output Codes method is a technique that allows a multi-class classification problem to be reframed as multiple binary classification problems, allowing the use of native binary classification models to be used directly. Unlike one-vs-rest and one-vs-one methods that offer a similar solution by dividing a multi-class classification problem into a fixed number of binary classification problems, the error-correcting output codes technique allows each class to be encoded as an arbitrary number of binary classification problems. When an overdetermined representation is used, it allows the extra models to act as "error-correction" predictions that can result in better predictive performance.

In algorithmic decision making, recourse refers to individuals' ability to systematically reverse an unfavorable decision made by an algorithm. Meanwhile, individuals subjected to a classification mechanism are incentivized to behave strategically in order to gain a system's approval. However, not all strategic behavior necessarily leads to adverse results: through appropriate mechanism design, strategic behavior can induce genuine improvement in an individual's qualifications. In this paper, we explore how to design a classifier that achieves high accuracy while providing recourse to strategic individuals so as to incentivize them to improve their features in non-manipulative ways. We capture these dynamics using a two-stage game: first, the mechanism designer publishes a classifier, with the goal of optimizing classification accuracy and providing recourse to incentivize individuals' improvement. Then, agents respond by potentially modifying their input features in order to obtain a favorable decision from the classifier, while trying to minimize the cost of making such modifications. Under this model, we provide analytical results characterizing the equilibrium strategies for both the mechanism designer and the agents. Our empirical results show the effectiveness of our mechanism in three real-world datasets: compared to a baseline classifier that only considers individuals' strategic behavior without explicitly incentivizing improvement, our algorithm can provide recourse to a much higher fraction of individuals in the direction of improvement while maintaining relatively high prediction accuracy. We also show that our algorithm can effectively mitigate disparities caused by differences in manipulation costs. Our results provide insights for designing a machine learning model that focuses not only on the static distribution as of now, but also tries to encourage future improvement.

We consider a fixed-design linear regression in the meta-learning model of Baxter (2000) and establish a problem-dependent finite-sample lower bound on the transfer risk (risk on a newly observed task) valid for all estimators. Moreover, we prove that a weighted form of a biased regularization - a popular technique in transfer and meta-learning - is optimal, i.e. it enjoys a problem-dependent upper bound on the risk matching our lower bound up to a constant. Thus, our bounds characterize meta-learning linear regression problems and reveal a fine-grained dependency on the task structure. Our characterization suggests that in the non-asymptotic regime, for a sufficiently large number of tasks, meta-learning can be considerably superior to a single-task learning. Finally, we propose a practical adaptation of the optimal estimator through Expectation-Maximization procedure and show its effectiveness in series of experiments.

With a focus on linear models with smooth functional covariates, we propose a penalization framework (SACR) based on the nonzero centered ridge, where the center of the penalty is optimally reweighted in a supervised way, starting from the ordinary ridge solution as the initial centerfunction. In particular, we introduce a convex formulation that jointly estimates the model's coefficients and the weight function, with a roughness penalty on the centerfunction and constraints on the weights in order to recover a possibly smooth and/or sparse solution. This allows for a non-iterative and continuous variable selection mechanism, as the weight function can either inflate or deflate the initial center, in order to target the penalty towards a suitable center, with the objective to reduce the unwanted shrinkage on the nonzero coefficients, instead of uniformly shrinking the whole coefficient function. As empirical evidence of the interpretability and predictive power of our method, we provide a simulation study and two real world spectroscopy applications with both classification and regression.

Using prototype methods to reduce the size of training datasets can drastically reduce the computational cost of classification with instance-based learning algorithms like the k-Nearest Neighbour classifier. The number and distribution of prototypes required for the classifier to match its original performance is intimately related to the geometry of the training data. As a result, it is often difficult to find the optimal prototypes for a given dataset, and heuristic algorithms are used instead. However, we consider a particularly challenging setting where commonly used heuristic algorithms fail to find suitable prototypes and show that the optimal prototypes can instead be found analytically. We also propose an algorithm for finding nearly-optimal prototypes in this setting, and use it to empirically validate the theoretical results.

In this paper we revisit the classical problem of nonparametric regression, but impose local differential privacy constraints. Under such constraints, the raw data $(X_1,Y_1),\ldots,(X_n,Y_n)$, taking values in $\mathbb{R}^d \times \mathbb{R}$, cannot be directly observed, and all estimators are functions of the randomised output from a suitable privacy mechanism. The statistician is free to choose the form of the privacy mechanism, and here we add Laplace distributed noise to a discretisation of the location of a feature vector $X_i$ and to the value of its response variable $Y_i$. Based on this randomised data, we design a novel estimator of the regression function, which can be viewed as a privatised version of the well-studied partitioning regression estimator. The main result is that the estimator is strongly universally consistent. Our methods and analysis also give rise to a strongly universally consistent binary classification rule for locally differentially private data.