Recently, a quantum algorithm for clustering based on the k -means algorithm has been proposed by Kerenidis, Landman, Luongo and Prakash. Based on their work, we propose a quantum expectation-maximization (EM) algorithm for Gaussian mixture models (GMMs). The robustness and quantum speedup of the algorithm is demonstrated. We also show numerically the advantage of GMM over k-means for nontrivial cluster data. I. INTRODUCTION Quantum computing has attracted much attention since the discovery of Shor's algorithm [1, 2]. Recently, with the rapid developments in machine learning, physicists have started to consider utilizing quantum computers for machine learning applications [3-8]. As a result, quantum machine learning has emerged as an interdisciplinary field between quantum computing and machine learning. Furthermore, a quantum algorithm for the k - means algorithm [9, 10] with proven quantum speedup was proposed [11]. The k -means algorithm is an essential tool in many machine learning applications [9, 10].

Learning with label proportions(LLP), which seeks an instance-level classifier merely based on bag-level label proportions, is a new paradigm in machine learning that addresses the classification of instances [1, 2, 3]. In LLP, we only know the proportions of examples belonging to different classes in each bag; however the labels of the instances are unknown. From the binary classification perspective, the task of LLP is to learn a classifier to classify the unknown label instance as either positive class or negative class. The formulation that learning with label proportions has been first proposed by Kuck et al. in [1], which can be used for political elections analysis. In the case of politician polls, each candidate may have a group of loyal voters and some swing voters. They may know the vague proportion of votes cast in each district; however, they usually do not know the vote of each person. Since the candidates have limited resources, they have to analyze political elections and consider which kind of voters they should focus on so as to maximize their interests. To date, LLP has been applied to forecasting revenue [4], image classification [5, 6], video event detection [7], demographics mining [8] and privacy protection [9]. Figure 1 illustrates the binary classification problem in LLP.

Recently, many methods to reduce neural networks uncertainty have been proposed. However, most of the techniques used in these solutions usually present severe drawbacks. In this paper, we argue that neural networks low out-of-distribution detection performance is mainly due to the SoftMax loss anisotropy. Therefore, we built an isotropic loss to reduce neural networks uncertainty in a fast, scalable, turnkey, and native approach. Our experiments show that replacing SoftMax with the proposed loss does not affect classification accuracy. Moreover, our proposal overcomes ODIN typically by a large margin while producing usually competitive results against a state-of-the-art Mahalanobis method despite avoiding their limitations. Hence, neural networks uncertainty may be significantly reduced by a simple loss change without relying on special procedures such as data augmentation, adversarial training/validation, ensembles, or additional classification/regression models.

An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and rely on annotation schemes with low inter-rater reliability, limiting generalizability to conversations spanning multiple domains. To address these gaps, we created a new Response Quality annotation scheme, based on which we developed turn-level User Satisfaction metric. We introduced five new domain-independent feature sets and experimented with six machine learning models to estimate the new satisfaction metric. Using Response Quality annotation scheme, across randomly sampled single and multi-turn conversations from 26 domains, we achieved high inter-annotator agreement (Spearman's rho 0.94). The Response Quality labels were highly correlated (0.76) with explicit turn-level user ratings. Gradient boosting regression achieved best correlation of ~0.79 between predicted and annotated user satisfaction labels. Multi Layer Perceptron and Gradient Boosting regression models generalized to an unseen domain better (linear correlation 0.67) than other models. Finally, our ablation study verified that our novel features significantly improved model performance.

This is a Lasso; it is used to pick and capture animals. As a non-native English speaker, my first exposure to this word is in supervised learning. In this LASSO data science tutorial, we discuss the strengths of the Lasso logistic regression by stepping through how to apply this useful statistical method for classification problems in R and how the Lasso can be "similarly" used to pick and select input variables that are relevant to the classification problem at hand. Data analysts and data scientists use different regression methods for different kinds of analytics problems. One of the most talked-about methods is the Lasso.

With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction. Unlike other financial networks, such as stock and currency trading, blockchain based cryptocurrencies have the entire transaction graph accessible to the public (i.e., all transactions can be downloaded and analyzed). A natural question is then to ask whether the dynamics of the transaction graph impacts the price of the underlying cryptocurrency. We show that standard graph features such as degree distribution of the transaction graph may not be sufficient to capture network dynamics and its potential impact on fluctuations of Bitcoin price. In contrast, the new graph associated topological features computed using the tools of persistent homology, are found to exhibit a high utility for predicting Bitcoin price dynamics. %explain higher order interactions among the nodes in Blockchain graphs and can be used to build much more accurate price prediction models. Using the proposed persistent homology-based techniques, we offer a new elegant, easily extendable and computationally light approach for graph representation learning on Blockchain.

Email messages have been an important mode of communication, not only for work, but also for social interactions and marketing. When messages have time sensitive information, it becomes relevant for the sender to know what is the expected time within which the email will be read by the recipient. In this paper we use a survival analysis framework to predict the time to open an email once it has been received. We use the Cox Proportional Hazards (CoxPH) model that offers a way to combine various features that might affect the event of opening an email. As an extension, we also apply a mixture model (MM) approach to CoxPH that distinguishes between recipients, based on a latent state of how prone to opening the messages each individual is. We compare our approach with standard classification and regression models. While the classification model provides predictions on the likelihood of an email being opened, the regression model provides prediction of the real-valued time to open. The use of survival analysis based methods allows us to jointly model both the open event as well as the time-to-open. We experimented on a large real-world dataset of marketing emails sent in a 3-month time duration. The mixture model achieves the best accuracy on our data where a high proportion of email messages go unopened.

Class imbalance problem is commonly faced while developing machine learning models for real-life issues. Due to this problem, the fitted model tends to be biased towards the majority class data, which leads to lower precision, recall, AUC, F1, G-mean score. Several researches have been done to tackle this problem, most of which employed resampling, i.e. oversampling and undersampling techniques to bring the required balance in the data. In this paper, we propose neural network based algorithms for undersampling. Then we resampled several class imbalanced data using our algorithms and also some other popular resampling techniques. Afterwards we classified these undersampled data using some common classifier. We found out that our resampling approaches outperform most other resampling techniques in terms of both AUC, F1 and G-mean score.

Vogelstein 1,4 1 Department of Biomedical Engineering, Johns Hopkins University 2 Department of Applied Economics and Statistics, University of Delaware 3 Center for the Developing Brain, Child Mind Institute 4 Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University A fundamental problem in statistical data analysis is testing whether two phenomena are related. When the phenomena in question are time series, many challenges emerge. The first is defining a dependence measure between time series at the population level, as well as a sample level test statistic. The second is computing or estimating the distribution of this test statistic under the null, as the permutation test procedure is invalid for most time series structures. This work aims to address these challenges by combining distance correlation and multiscale graph correlation (MGC) from independence testing literature and block permutation testing from time series analysis. Two hypothesis tests for testing the independence of time series are proposed. These procedures also characterize whether the dependence relationship between the series is linear or nonlinear, and the time lag at which this dependence is maximized. For strictly stationary auto-regressive moving average (ARMA) processes, the proposed independence tests are proven valid and consistent. Finally, neural connectivity in the brain is analyzed using fMRI data, revealing linear dependence of signals within the visual network and default mode network, and nonlinear relationships in other regions. This work opens up new theoretical and practical directions for many modern time series analysis problems. 1 Introduction Time series data are ubiquitous in fields such as neuroscience, finance, and sociology .

When several predictors have been trained to solve the same classification task, a second level of algorithmic procedure is necessary to reconcile the classifier predictions and deliver a single one. Such a procedure is known as classifier combination, fusion or aggregation. When each individual classifier is trained using the same training algorithm (but under different circumstances) the aggregation procedure is referred to as an ensemble method. When each classifier may be generated by different training algorithms, the aggregation procedure is referred to as a multiple classifier system. In both cases, the set of individual classifiers is called a classifier ensemble. Classifier combination comes either from a choice of the programmer or is imposed by context. In the first case, combination is meant to increase classification performances by either increasing the learning capacity or mitigating 1 arXiv:1908.06475v1