We develop a general non-asymptotic analysis of learning rates in kernel ridge regression (KRR), applicable for arbitrary Mercer kernels with multi-dimensional support. Our analysis is based on an operator-theoretic framework, at the core of which lies two error bounds under reproducing kernel Hilbert space norms encompassing a general class of kernels and regression functions, with remarkable extensibility to various inferential goals through augmenting results. When applied to KRR estimators, our analysis leads to error bounds under the stronger supremum norm, in addition to the commonly studied weighted $L_2$ norm; in a concrete example specialized to the Mat\'ern kernel, the established bounds recover the nearly minimax optimal rates. The wide applicability of our analysis is further demonstrated through two new theoretical results: (1) non-asymptotic learning rates for mixed partial derivatives of KRR estimators, and (2) a non-asymptotic characterization of the posterior variances of Gaussian processes, which corresponds to uncertainty quantification in kernel methods and nonparametric Bayes.

Amazon's controversial facial recognition technology has incorrectly matched more than 100 photos of politicians in the UK and US to police mugshots, new tests have revealed. Amazon Rekognition uses artificial intelligence software to identify individuals from their facial structure. Customers include law enforcement and US government agencies like Immigration and Custome Enforcement (ICE). It is not the first time the software's accuracy has been called into question. In July 2018, the American Civil Liberties Union (ACLU) found 28 false matches between US Congress members and pictures of people arrested for a crime.

Unnecessary restrictions on blood donors should be removed to maximize the blood and plasma available for use. With a vaccine for coronavirus disease 2019 (COVID-19) likely more than a year away, we must identify effective therapies for patients now. One promising approach is the use of plasma from patients who have recovered from COVID-19 (1, 2). To facilitate this strategy, the U.S. Food and Drug Administration (FDA) recently revised some of the restrictions on blood donation, including a decrease in deferral time for men who have sex with men (MSM) to 3 months (3). This is a positive change to an outdated guideline, but it does not go far enough.

Electroencephalograms (EEG) are a noninvasive longstanding medical modality that measures the brain's activity by recording the electromagnetic field at the scalp. Since its creation, EEG has played a fundamental role in understanding several major neurological disorders, by analyzing their manifestation into brain rhythms. For example, the study of deceases such as depression, age-related cognitive deterioration, epilepsy, anxiety disorders and subnormal brain development in children have benefited from this technology. The typical brain rhythms are distinguished by their different frequency ranges, called delta (δ) within the range 0.5 to 4Hz, theta (θ) within the range 4 to 7.5Hz, alpha (α) within the range 8 to 13Hz, beta (β) within the range 14 to 30Hz, and gamma (γ) within the range 30 to 64Hz. In this study, we focus on the brain rhythm called mu (µ) within the range 7.5 to 11.5Hz. Mu-waves are considered to emerge naturally and may convey information about what the functioning of brain hierarchies [1]. According to [2], there exist three historical theoretical hypotheses to explaining the mu-brain rhythm: i) the neuronal hyperexcitability related to the rolandic cortex; ii) the superficial cortical inhibition explaining its suppression with motor activity; and iii) the somatosensory cortical idling, related to the afference-dependent phenomenon.

Given the surge in popularity of mutual funds (including exchange-traded funds (ETFs)) as a diversified financial investment, a vast variety of mutual funds from various investment management firms and diversification strategies have become available in the market. Identifying similar mutual funds among such a wide landscape of mutual funds has become more important than ever because of many applications ranging from sales and marketing to portfolio replication, portfolio diversification and tax loss harvesting. The current best method is data-vendor provided categorization which usually relies on curation by human experts with the help of available data. In this work, we establish that an industry wide well-regarded categorization system is learnable using machine learning and largely reproducible, and in turn constructing a truly data-driven categorization. We discuss the intellectual challenges in learning this man-made system, our results and their implications.

If you believed everything you read, artificial intelligence (AI) is the savior of cybersecurity. According to Capgemini, 80% of companies are counting on AI to help identify threats and thwart attacks. That's a big ask to live up to because, in reality, few nonexperts really understand the value of AI to security or whether the technology can effectively address information security's many potential use cases. A cynic would call out the proliferation of claims about using AI for what it is -- marketing hype. Even the use of the term "AI" is misleading.

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with an exclusive testing approach of otherwise inaccessible black-box systems. Especially the interaction between safety and security is a central challenge, as security violations can lead to compromised safety. The contribution of this paper to addressing both safety and security within a single concept of protection applicable during the operation of ML systems is active monitoring of the behaviour and the operational context of the data-driven system based on distance measures of the Empirical Cumulative Distribution Function (ECDF). We investigate abstract datasets (XOR, Spiral, Circle) and current security-specific datasets for intrusion detection (CICIDS2017) of simulated network traffic, using distributional shift detection measures including the Kolmogorov-Smirnov, Kuiper, Anderson-Darling, Wasserstein and mixed Wasserstein-Anderson-Darling measures. Our preliminary findings indicate that the approach can provide a basis for detecting whether the application context of an ML component is valid in the safety-security. Our preliminary code and results are available at https://github.com/ISorokos/SafeML.

LWF chain graphs combine directed acyclic graphs and undirected graphs. We present a PC-like algorithm that finds the structure of chain graphs under the faithfulness assumption to resolve the problem of scalability of the proposed algorithm by Studeny (1997). We prove that our PC-like algorithm is order dependent, in the sense that the output can depend on the order in which the variables are given. This order dependence can be very pronounced in high-dimensional settings. We propose two modifications of the PC-like algorithm that remove part or all of this order dependence. Simulation results under a variety of settings demonstrate the competitive performance of the PC-like algorithms in comparison with the decomposition-based method, called LCD algorithm, proposed by Ma et al. (2008) in low-dimensional settings and improved performance in high-dimensional settings.

With both the introduction of new ways of storing, sharing and streaming data and the drastic development of the capacity of computers to handle large computations, the conception of models have changed. Mathematical models were first designed following prior ideas or conjectures from physical or biological models, then tested by designing experiments to test the validity of the ideas of their inventors. The model holds until new observations enable to reject its assumptions. The so-called Big Data's area introduced a new paradigm. The observed data convey enough information to understand the complexity of real life and the more the data, the better the description of the reality. Hence building models optimised to fit the data has become an efficient way to obtain generalizable models able to describe and forecast the real world. In this framework, the principle of supervised machine learning is to build a decision rule from a set of labeled examples called the learning sample, that fits the data.

Machine learning models have spread to almost every area of life. They are successfully applied in biology, medicine, finance, physics, and other fields. With modern software it is easy to train even a~complex model that fits the training data and results in high accuracy on the test set. The problem arises when models fail confronted with real-world data. This paper describes methodology and tools for model-agnostic audit. Introduced techniques facilitate assessing and comparing the goodness of fit and performance of models. In~addition, they may be used for the analysis of the similarity of residuals and for identification of~outliers and influential observations. The examination is carried out by diagnostic scores and visual verification. Presented methods were implemented in the auditor package for R. Due to flexible and~consistent grammar, it is simple to validate models of any classes.