Active search (AS) on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights under a query budget. However, in most networks, nodes, topology and edge weights are all initially unknown. We introduce selective harvesting, a variant of AS where the next node to be queried must be chosen among the neighbors of the current queried node set; the available training data for deciding which node to query is restricted to the subgraph induced by the queried set (and their node attributes) and their neighbors (without any node or edge attributes). Therefore, selective harvesting is a sequential decision problem, where we must decide which node to query at each step. A classifier trained in this scenario suffers from a tunnel vision effect: without recourse to independent sampling, the urge to query promising nodes forces classifiers to gather increasingly biased training data, which we show significantly hurts the performance of AS methods and standard classifiers. We find that it is possible to collect a much larger set of targets by using multiple classifiers, not by combining their predictions as an ensemble, but switching between classifiers used at each step, as a way to ease the tunnel vision effect. We discover that switching classifiers collects more targets by (a) diversifying the training data and (b) broadening the choices of nodes that can be queried next. This highlights an exploration, exploitation, and diversification trade-off in our problem that goes beyond the exploration and exploitation duality found in classic sequential decision problems. From these observations we propose D3TS, a method based on multi-armed bandits for non-stationary stochastic processes that enforces classifier diversity, matching or exceeding the performance of competing methods on seven real network datasets in our evaluation.

We develop an online learning method for prediction, which is important in problems with large and/or streaming data sets. We formulate the learning approach using a covariance-fitting methodology, and show that the resulting predictor has desirable computational and distribution-free properties: It is implemented online with a runtime that scales linearly in the number of samples; has a constant memory requirement; avoids local minima problems; and prunes away redundant feature dimensions without relying on restrictive assumptions on the data distribution. In conjunction with the split conformal approach, it also produces distribution-free prediction confidence intervals in a computationally efficient manner. The method is demonstrated on both real and synthetic datasets.

Sejun Park Eunho Y ang † Jinwoo Shin November 4, 2017 Abstract Learning parameters of latent graphical models (GM) is inherently much harder than that of no-latent ones since the latent variables make the corresponding log-likelihood non-concave. Nevertheless, expectation-maximization schemes are popularly used in practice, but they are typically stuck in local optima. In the recent years, the method of moments have provided a refreshing angle for resolving the non-convex issue, but it is applicable to a quite limited class of latent GMs. In this paper, we aim for enhancing its power via enlarging such a class of latent GMs. To this end, we introduce two novel concepts, coined marginalization and conditioning, which can reduce the problem of learning a larger GM to that of a smaller one. More importantly, they lead to a sequential learning framework that repeatedly increases the learning portion of given latent GM, and thus covers a significantly broader and more complicated class of loopy latent GMs which include convolutional and random regular models. 1 Introduction Graphical models (GM) are succinct representation of a joint distribution on a graph where each node corresponds to a random variable and each edge represents the conditional independence between random variables. GM have been successfully applied for various fields including information theory [12, 19], physics [24] and machine learning [18, 11]. Introducing latent variables to GM has been popular approaches for enhancing their representation powers in recent deep models, e.g., convolutional/restricted/deep Boltzmann machines [20, 27]. Furthermore, they are inevitable in certain scenarios when a part of samples is missing, e.g., see [10]. However, learning parameters of latent GMs is significantly harder than that of no-latent ones since the latent variables make the corresponding negative log-likelihood non-convex.

In this article, I present a few modern techniques that have been used in various business contexts, comparing performance with traditional methods. The advanced techniques in question are math-free, innovative, efficiently process large amounts of unstructured data, and are robust and scalable. Implementations in Python, R, Julia and Perl are provided, but here we focus on an Excel version that does not even require any Excel macros, coding, plug-ins, or anything other than the most basic version of Excel. It is actually easily implemented in standard, basic SQL too, and we invite readers to work on an SQL version. In short, we offer here an Excel template for machine learning and statistical computing, and it is quite powerful for an Excel spreadsheet.

Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS). Based on a motor learning theoretical context and on the failure of neuroelectric brain–computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure. Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)--two of them in permanent CLIS and two entering the CLIS without reliable means of communication--learned to answer personal questions with known answers and open questions all requiring a "yes" or "no" thought using frontocentral oxygenation changes measured with fNIRS. Three patients completed more than 46 sessions spread over several weeks, and one patient (patient W) completed 20 sessions. Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine (SVM) resulted in an above-chance-level correct response rate over 70%.

Machines, you may have heard, are coming for all the jobs. Artificial intelligence handles insurance claims and basic bookkeeping, manages investment portfolios, does legal research, and performs basic HR tasks. Human labor doesn't stand a chance against them--after the "automation apocalypse," only those with spectacular abilities and the owners of the robots will thrive. But before you start campaigning for a universal basic income and set up a bunker, you might want to also familiarize yourself with the competing theory: In the long run, we're going to be just fine. Our modern fear that robots will steal all the jobs fits a classic script. Nearly 500 years ago, Queen Elizabeth I cited the same fear when she denied an English inventor named William Lee a patent for an automated knitting contraption.

It's 3/14, also known as Pi Day – a mathematics holiday to celebrate the irrational, transcendental number we learned in school, for the most part, to calculate the circumference or area of circles. While there are a number of fulfilling Pi(e) related activities you can indulge in, from feasting on scrumptious pies to chasing down the value of Pi (good luck!), it is also an apt moment to turn attention to where children across the world stand in mathematics achievement and other learning outcomes. While countries have made impressive gains in access to education, a recurring theme is that not nearly enough learning is happening. The 2018 World Development Report (WDR) takes on the learning crisis and its possible underlying factors. The report also takes stock of a growing evidence base to identify key principles and effective interventions to improve learning, challenges in taking successful interventions to scale, and strategies to overcome those challenges.

Tenth and ninth grade engineering students at The Frisch School in Paramus got to watch a live broadcast of a robotics surgery on Wed., March 8, 2017. High school engineering students in Paramus last week were privy to the intricate workings of a robotics surgery, as it was broadcast live before them from an operating room in Hackensack. About 160 ninth and tenth grade students at The Frisch School on Wednesday watched as Dr. Michael Stifelman, chair of urology and director of robotic surgery at Hackensack University Medical Center, performed an hourlong robotic partial nephrectomy. As students looked on, Stifelman explained how he was preparing to remove a tumor from a kidney. The robotics equipment he was using, he said, enabled him to perform a less-invasive procedure.

Blackboard Learn's advanced assessment capabilities match or exceed those of many dedicated commercial online testing platforms. In this session, Paul Barney, Supervisor of Curriculum and Assessment at the Higher Colleges of Technology, and Azim Ahmed, Ed Tech Senior Project Manager, demonstrate how their institution uses Blackboard to reliably and securely test up to 4000 students per administration. The session addresses the three biggest challenges institutions can encounter, solutions to those challenges, and tips and advice for a smooth and problem-free large-scale administration.

In the past years, a collection of hardware, software and online service have managed to bring changes and reforms to classrooms and teaching methods. But the true disruption of education is yet to arrive. Artificial Intelligence has proven its role as a game changing factor in an increasing number of fields, causing transformations unimaginable in the past. It's now showing glimmers of how it might forever change the learning process, one of the oldest skills that mankind has mastered. Here's how AI and its derivatives are gradually finding their way into the classroom, and beyond.