Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.

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An introduction to variable and feature selection

We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering.

Data clustering: 50 years beyond K-means

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Convex Analysisの二,三の進展について

This paper is concerned with algorithms for online learning of expert advice and contains both theoretical and empirical results. In the first part of the paper we present new online algorithms for combining expert opinions. Unlike most previous algorithms, our algorithms “smoothly” adjust their learning rates without forgetting past performance of the experts. Our analysis show that the proposed algorithms enjoy better asymptotic performance guarantees than all known bounds in the two important settings of absolute and 0/1 loss functions. Specifically, in the case of the absolute loss, we achieve an asymptotic bound of ( 3 √ 2 4 (1 + 1 3 √ α ) + o(1)) · √ lnN · L∗ on the regret, where α > 1 is any real, L is the loss of the best expert and N is the number of experts. This bound improves on all known bounds in this setting including the recent (2.83 + o(1)) · √ lnN · L∗ bound achieved by Auer et al. In the case of 0/1-loss we achieve an expected bound of (( √ 2 + 1/ √ 2 · α) + o(1)) · √ lnN · L∗. Here again, this is the best known bound to-date. In the second part of the paper we report on empirical study where we evaluate and compare the performance of our algorithms and other known expert advice algorithms using both synthetic and real data. Our empirical results with respect to a real-life prediction problem indicate that smooth algorithms for expert advice, and in particular the proposed algorithms, have advantage over other expert advice algorithms.

Smooth Online Learning of Expert Advice

In this paper we focus on the problem of online portfolio selection with transaction costs. We tackle this problem using a novel approach for combining the predictions of long-term experts with those of short-term experts so as to effectively reduce transaction costs. We prove that the new strategy maintains bounded regret relative to the performance of the best possible combination (switching times) of the long-and short-term experts. We empirically validate our approach on several standard benchmark datasets. These studies indicate that the proposed approach achieves state-ofthe-art performance.

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Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs

Online-learning research has mainly been focusing on minimizing one objective function. In many real-world applications, however, several objective functions have to be considered simultaneously. Recently, an algorithm for dealing with several objective functions in the i.i.d. case has been presented. In this paper, we extend the multi-objective framework to the case of stationary and ergodic processes, thus allowing dependencies among observations. We first identify an asymptomatic lower bound for any prediction strategy and then present an algorithm whose predictions achieve the optimal solution while fulfilling any continuous and convex constraining criterion.

Multi-Objective Non-parametric Sequential Prediction

Due to the comprehensive nature of this paper, it has been updated and split into two separate papers:"A Framework For Benchmarking Class-out-of-distribution Detection And Its Application To ImageNet"and"What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers". We recommend reading them instead. Deep neural networks must be equipped with an uncertainty estimation mechanism when deployed for risk-sensitive tasks. This paper studies the relationship between deep architectures and their training regimes with their corresponding selective prediction and uncertainty estimation performance. We consider both in-distribution uncertainties and class-out-of-distribution ones. Moreover, we consider some of the most popular estimation performance metrics previously proposed including AUROC, ECE, AURC, and coverage for selective accuracy constraint. We present a novel and comprehensive study of selective prediction and the uncertainty estimation performance of 484 existing pretrained deep ImageNet classifiers that are available at popular repositories. We identify numerous and previously unknown factors that affect uncertainty estimation and examine the relationships between the different metrics. We find that distillation-based training regimes consistently yield better uncertainty estimations than other training schemes such as vanilla training, pretraining on a larger dataset and adversarial training. We also provide strong empirical evidence showing that ViT is by far the most superior architecture in terms of uncertainty estimation performance, judging by any aspect, in both in-distribution and class-out-of-distribution scenarios.

Which models are innately best at uncertainty estimation?

We provide a general purpose Matlab tool for performance evaluation of supervised learning algorithms. Given any existing implementation of a classifler learning algorithm, our Generic-CV tool operates the algorithm, tunes its hyper-parameters and evaluates its performance on any given dataset. The tool is generic in the sense that it easily accommodates new learning algorithms, without any need for programming, and can operate Matlab implementations as well as ‘executables’. In addition, we provide a distributed version of our tool that can utilize multiple computers and in principle can speed up the enormous running time required for performance evaluation by several orders of magnitude.

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Ran El-Yaniv

Papers

Smooth Online Learning of Expert Advice

Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs

Multi-Objective Non-parametric Sequential Prediction

Which models are innately best at uncertainty estimation?

A Generic Tool for Performance Evaluation of Supervised Learning Algorithms