Some example of the task are the zero-one loss classification where the predictor suffers a loss of one when making incorrect prediction and zero otherwise as well as the ordinal classification (also known as ordinal regression) where the predictor suffers a loss that increases as the prediction moves away from the true label. Empirical risk minimization (ERM) (Vapnik, 1992) is a standard approach for solving general multiclass classification problems by finding the classifier that minimizes a loss metric over the training data. However, since directly minimizing this loss over training data within the ERM framework is generally NPhard (Steinwart and Christmann, 2008), convex surrogate losses that can be efficiently optimized are employed to approximate the loss. Constructing surrogate losses for binary classification has been well studied, resulting in surrogate losses that enjoy desirable theoretical properties and good performance in practice. Among the popular examples are the logarithmic loss, which is minimized by the logistic regression classifier (McCullagh and Nelder, 1989), and the hinge loss, which is minimized by the support vector machine (SVM) (Boser et al., 1992; Cortes and Vapnik, 1995).

Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performance. Enriching the trust between these systems and people will accelerate this integration process. Many medical and retail banking/finance applications use state-of-the-art machine learning techniques to predict certain aspects of new instances. Tree ensembles, like random forests, are widely acceptable solutions on these tasks, while at the same time they are avoided due to their black-box uninterpretable nature, creating an unreasonable paradox. In this paper, we provide a sequence of actions for shedding light on the predictions of the misjudged family of tree ensemble algorithms. Using classic unsupervised learning techniques and an enhanced similarity metric, to wander among transparent trees inside a forest following breadcrumbs, the interpretable essence of tree ensembles arises. An explanation provided by these systems using our approach, which we call "LionForests", can be a simple, comprehensive rule.

Sparse convex clustering is to cluster observations and conduct variable selection simultaneously in the framework of convex clustering. Although the weighted $L_1$ norm as the regularization term is usually employed in the sparse convex clustering, this increases the dependence on the data and reduces the estimation accuracy if the sample size is not sufficient. To tackle these problems, this paper proposes a Bayesian sparse convex clustering via the idea of Bayesian lasso and global-local shrinkage priors. We introduce Gibbs sampling algorithms for our method using scale mixtures of normals. The effectiveness of the proposed methods is shown in simulation studies and a real data analysis.

This paper studies the theoretical predictive properties of classes of forecast combination methods. The study is motivated by the recently developed Bayesian framework for synthesizing predictive densities: Bayesian predictive synthesis. A novel strategy based on continuous time stochastic processes is proposed and developed, where the combined predictive error processes are expressed as stochastic differential equations, evaluated using Ito's lemma. We show that a subclass of synthesis functions under Bayesian predictive synthesis, which we categorize as non-linear synthesis, entails an extra term that "corrects" the bias from misspecification and dependence in the predictive error process, effectively improving forecasts. Theoretical properties are examined and shown that this subclass improves the expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions. We discuss the conditions for which this subclass outperforms others, and its implications for developing forecast combination methods. A finite sample simulation study is presented to illustrate our results.

Flow-based generative models leverage invertible generator functions to fit a distribution to the training data using maximum likelihood. Despite their use in several application domains, robustness of these models to adversarial attacks has hardly been explored. In this paper, we study adversarial robustness of flow-based generative models both theoretically (for some simple models) and empirically (for more complex ones). First, we consider a linear flow-based generative model and compute optimal sample-specific and universal adversarial perturbations that maximally decrease the likelihood scores. Using this result, we study the robustness of the well-known adversarial training procedure, where we characterize the fundamental trade-off between model robustness and accuracy. Next, we empirically study the robustness of two prominent deep, non-linear, flow-based generative models, namely GLOW and RealNVP. We design two types of adversarial attacks; one that minimizes the likelihood scores of in-distribution samples, while the other that maximizes the likelihood scores of out-of-distribution ones. We find that GLOW and RealNVP are extremely sensitive to both types of attacks. Finally, using a hybrid adversarial training procedure, we significantly boost the robustness of these generative models.

A BSTRACT Recent advances in deep learning theory have evoked the study of generalizabil-ity across different local minima of deep neural networks (DNNs). While current work focused on either discovering properties of good local minima or developing regularization techniques to induce good local minima, no approach exists that can tackle both problems. We achieve these two goals successfully in a unified manner. Specifically, based on the Fisher information we propose a metric both strongly indicative of generalizability of local minima and effectively applied as a practical regularizer. We provide theoretical analysis including a generalization bound and empirically demonstrate the success of our approach in both capturing and improving the generalizability of DNNs. Experiments are performed on CIFAR-10 and CIFAR-100 for various network architectures. 1 I NTRODUCTION Recently, there has been a surge in the interest of acquiring a theoretical understanding over deep neural network's behavior. Breakthroughs have been made in characterizing the optimization process, showing that learning algorithms such as stochastic gradient descent (SGD) tend to end up in one of the many local minima which have close-to-zero training loss (Choromanska et al., 2015; Dauphin et al., 2014; Kawaguchi, 2016; Nguyen & Hein, 2018; Du et al., 2018). It is, therefore, natural to ask two closely related questions: (a) What kind of local minima can generalize better? To our knowledge, existing work focused only on one of the two questions. For the "what" question, various definitions of "flatness/sharpness" have been introduced and analyzed (Keskar et al., 2017; Neyshabur et al., 2018; 2017; Wu et al., 2017; Liang et al., 2017).

Assortment optimization is an important problem that arises in many practical applications such as retailing and online advertising where the goal is to find a subset of products from a universe of substitutable products that maximize a seller's expected revenue. The demand and the revenue depend on the substitution behavior of the customers that is captured by a choice model. One of the key challenges is to find the right model for the customer substitution behavior. Many parametric random utility based models have been considered in the literature to capture substitution. However, in all these models, the probability of purchase increases as we add more options to the assortment. This is not true in general and in many settings, the probability of purchase may decrease if we add more products to the assortment, referred to as the choice overload. In this paper we attempt to address these serious limitations and propose a generalization of the Markov chain based choice model considered in Blanchet et al. In particular, we handle dynamic preferences and the choice overload phenomenon using a Markovian comparison model that is a generalization of the Markovian substitution framework of Blanchet et al. The Markovian comparison framework allows us to implicitly model the search cost in the choice process and thereby, modeling both dynamic preferences as well as the choice overload phenomenon. We consider the assortment optimization problem for the special case of our generalized Markov chain model where the underlying Markov chain is rank-1 (this is a generalization of the Multinomial Logit model). We show that the assortment optimization problem under this model is NP-hard and present a fully polynomial-time approximation scheme (FPTAS) for this problem.

--Wide spread of satirical news in online communities is an ongoing trend. The nature of satires are so inherently ambiguous that sometimes it's too hard even for humans to understand whether it's actually satire or not. So, research interest has grown in this field. The purpose of this research is to detect Bangla satirical news spread in online news portals as well as social media. In this paper we propose a hybrid technique for extracting feature from text documents combining Word2V ecand TF-IDF. Using our proposed feature extraction technique, with standard CNN architecture we could detect whether a Bangla text document is satire or not with an accuracy of more than 96%. Satires can be considered as a literary form which involves a delicate balance between criticism and humor.

Despite the success of supervised machine learning and deep learning, there's a school of thought that says that unsupervised learning has even greater potential. The learning of a supervised learning system is limited by its training; i.e., a supervised learning system can learn only those tasks that it's trained for. By contrast, an unsupervised system could theoretically achieve "artificial general intelligence," meaning the ability to learn any task a human can learn. If the biggest problem with supervised learning is the expense of labeling the training data, the biggest problem with unsupervised learning (where the data is not labeled) is that it often doesn't work very well. Nevertheless, unsupervised learning does have its uses: It can sometimes be good for reducing the dimensionality of a data set, exploring the pattern and structure of the data, finding groups of similar objects, and detecting outliers and other noise in the data.

How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code (ECML 2002)