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Deep Unsupervised Clustering with Clustered Generator Model
This paper addresses the problem of unsupervised clustering which remains one of the most fundamental challenges in machine learning and artificial intelligence. We propose the clustered generator model for clustering which cont ... Read More >
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Anomaly and Novelty detection for robust semi-supervised learning
Three important issues are often encountered in Supervised and Semi-Supervised Classification: class-memberships are unreliable for some training units (label noise), a proportion of observations might depart from the main struc ... Read More >
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How Likely are Ride-share Drivers to Earn a Living Wage? Large-scale Spatio-temp ...
Ride-sourcing or transportation network companies (TNCs) provide on-demand transportation service for compensation, connecting drivers of personal vehicles with passengers through smartphone applications. In this study, we consi ... Read More >
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A Normal Approximation Method for Statistics in Knockouts
The authors give an approximation method for Bayesian inference in arena model, which is focused on paired comparisons with eliminations and bifurcations. The approximation method simplifies the inference by reducing parameters ... Read More >
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Developments in statistical inference when assessing spatiotemporal disease clus ...
The tau statistic $\tau$ uses geolocation and, usually, symptom onset time to assess global spatiotemporal clustering from epidemiological data. We test different factors that could affect graphical hypothesis tests of clusterin ... Read More >
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An Algorithm for Distributed Bayesian Inference in Generalized Linear Models
Monte Carlo algorithms, such as Markov chain Monte Carlo (MCMC) and Hamiltonian Monte Carlo (HMC), are routinely used for Bayesian inference in generalized linear models; however, these algorithms are prohibitively slow in massi ... Read More >
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DRHotNet: An R package for detecting differential risk hotspots on a linear netw ...
One of the most common applications of spatial data analysis is detecting zones, at a certain investigation level, where a point-referenced event under study is especially concentrated. The detection of this kind of zones, which ... Read More >
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Learning with Good Feature Representations in Bandits and in RL with a Generativ ...
The construction by Du et al. (2019) implies that even if a learner is given linear features in $\mathbb R^d$ that approximate the rewards in a bandit with a uniform error of $\epsilon$, then searching for an action that is opti ... Read More >
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Benchmarking time series classification -- Functional data vs machine learning a ...
Time series classification problems have drawn increasing attention in the machine learning and statistical community. Closely related is the field of functional data analysis (FDA): it refers to the range of problems that deal ... Read More >
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Change point localization in dependent dynamic nonparametric random dot product ...
In this paper, we study the offline change point localization problem in a sequence of dependent nonparametric random dot product graphs. To be specific, assume that at every time point, a network is generated from a nonparametr ... Read More >