Conformal predictors are confidence predictors that result in prediction sets for all confidence levels. Thus, Conformal Prediction (CP) is a framework that complements the predictions of machine learning algorithms with reliable measures of confidence. Transductive Conformal Prediction (TCP) works in an online transductive setting, such that learning and prediction occur simultaneously. In this sense confidence in a prediction is tailored both to the previously seen objects (whose features and labels are known) and to the features of the new object, whose label is to be predicted. By conditioning on the new objects conformal predictors take account of how difficult a particular object is to label and adjust their confidence in the prediction accordingly, as opposed to having an overall error rate for labelling all new objects Vovk et al. (2005).

Bayesian approaches have been used extensively in scientific and engineering research to quantify uncertainty and extract information. However, its model-dependent nature means that when the a priori model is incomplete or unavailable, there is a severe risk that Bayesian approaches will yield misleading results. Here, we propose a universal model-free information extraction approach, capable of reliably recovering target signals from complex responses. This breakthrough leverages on a data-centric approach, whereby measured data is reconfigured to create an enriched observable space, which in turn is mapped to a well-adapted manifold, thereby detecting crucial information via a reconstructed low-rank phase-space. A Koopman operator is used to transform hidden and complex nonlinear dynamics to linear one, which enables us to detect hidden event of interest from rapidly evolving systems, and relate it to either unobservable stimulus or anomalous behaviour. Thanks to its data-driven nature, our method excludes completely any prior knowledge on governing dynamics. We benchmark the astonishing accuracy of our method on three diverse and challenging problems in: biology, medicine, and engineering. In all cases, our approach outperforms existing state-of-the-art methods, of both Bayesian and non-Bayesian type. By creating a new reliable information analysis paradigm, it is suitable for ubiquitous nonlinear dynamical systems or end-users with little expertise, which permits the unbiased understanding of various mechanisms in the real world.

Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches. We analyze the convergence rate of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

Kernel based methods have shown effective performance in many remote sensing classification tasks. However their performance significantly depend on its hyper-parameters. The conventional technique to estimate the parameter comes with high computational complexity. Thus, the objective of this letter is to propose an fast and efficient method to select the bandwidth parameter of the Gaussian kernel in the kernel based classification methods. The proposed method is developed based on the operators in the reproducing kernel Hilbert space and it is evaluated on Support vector machines and PerTurbo classification method. Experiments conducted with hyperspectral datasets show that our proposed method outperforms the state-of-art method in terms in computational time and classification performance.

The fundamental problem in short-text classification is \emph{feature sparseness} -- the lack of feature overlap between a trained model and a test instance to be classified. We propose \emph{ClassiNet} -- a network of classifiers trained for predicting missing features in a given instance, to overcome the feature sparseness problem. Using a set of unlabeled training instances, we first learn binary classifiers as feature predictors for predicting whether a particular feature occurs in a given instance. Next, each feature predictor is represented as a vertex $v_i$ in the ClassiNet where a one-to-one correspondence exists between feature predictors and vertices. The weight of the directed edge $e_{ij}$ connecting a vertex $v_i$ to a vertex $v_j$ represents the conditional probability that given $v_i$ exists in an instance, $v_j$ also exists in the same instance. We show that ClassiNets generalize word co-occurrence graphs by considering implicit co-occurrences between features. We extract numerous features from the trained ClassiNet to overcome feature sparseness. In particular, for a given instance $\vec{x}$, we find similar features from ClassiNet that did not appear in $\vec{x}$, and append those features in the representation of $\vec{x}$. Moreover, we propose a method based on graph propagation to find features that are indirectly related to a given short-text. We evaluate ClassiNets on several benchmark datasets for short-text classification. Our experimental results show that by using ClassiNet, we can statistically significantly improve the accuracy in short-text classification tasks, without having to use any external resources such as thesauri for finding related features.

Practitioners in medicine, business, political science, and other fields are increasingly aware that decisions should be personalized to each patient, customer, or voter. A given treatment (e.g. a drug or advertisement) should be administered only to those who will respond most positively, and certainly not to those who will be harmed by it. Individual-level treatment effects (ITEs) can be estimated with tools adapted from machine learning, but different models can yield contradictory estimates. Unlike risk prediction models, however, treatment effect models cannot be easily evaluated against each other using a held-out test set because the true treatment effect itself is never directly observed. Besides outcome prediction accuracy, several approaches that use held-out data to evaluate treatment effects models have been proposed, but they are largely unknown or cloistered within disciplines. We present a review of these approaches and demonstrate theoretical relationships among them. We demonstrate their behavior using simulations of both randomized and observational data. Based on our empirical and theoretical results, we advocate for the standardized use of estimated decision value for individual treatment effect model selection and validation.

One-class Support Vector Machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and widely adopted in both research as well as industrial applications. The biggest issue for OC-SVM is, however, the capability to operate with large and high-dimensional datasets due to inefficient features and optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or auto-encoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class SVM (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descend to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.

The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic ability, which allows an automatic estimation and comparison of set magnitudes. We show that when information about lower-complexity tasks is available, the higher-level proportional task becomes more accurate than when performed in isolation. Moreover, the multi-task model is able to generalize to unseen combinations of target/non-target objects. Consistently with behavioral evidence showing the interference of absolute number in the proportional task, the multi-task model no longer works when asked to provide the number of target objects in the scene.

Nowadays more and more data are gathered for detecting and preventing cyber attacks. In cyber security applications, data analytics techniques have to deal with active adversaries that try to deceive the data analytics models and avoid being detected. The existence of such adversarial behavior motivates the development of robust and resilient adversarial learning techniques for various tasks. Most of the previous work focused on adversarial classification techniques, which assumed the existence of a reasonably large amount of carefully labeled data instances. However, in practice, labeling the data instances often requires costly and time-consuming human expertise and becomes a significant bottleneck. Meanwhile, a large number of unlabeled instances can also be used to understand the adversaries' behavior. To address the above mentioned challenges, in this paper, we develop a novel grid based adversarial clustering algorithm. Our adversarial clustering algorithm is able to identify the core normal regions, and to draw defensive walls around the centers of the normal objects utilizing game theoretic ideas. Our algorithm also identifies sub-clusters of attack objects, the overlapping areas within clusters, and outliers which may be potential anomalies.

This article describes an implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs. We consider a hierarchical Bayesian approach with a prior that is constructed by truncating a series expansion of the soft label function using the graph Laplacian eigenfunctions as basis functions. We compare our truncated prior to the untruncated Laplacian based prior in simulated and real data examples to illustrate the improved scalability in terms of size of the underlying graph.