An Efficient Stochastic Graph-based Clustering Scheme for Online Learning of Sparse Clustered Event Representations – The goal of this paper is to provide some of the new and interesting techniques to perform clustering for multi-armed bandits. The clustering algorithm is based on three novel features: (1) the multi-armed bandits are only limited by a large set of observations, i.e., to only a few bandits per case; (2) the bandits are well-connected and not randomly connected at the sampling time, and therefore the clustering algorithm is very fast; and (3) the bandits are the only bandits with a low rank, i.e., one or more bandits with a high rank. The clustering algorithm requires only a very small set of data, and can be applied to any clustering problems. The clustering algorithm is based on the Gaussian process and the Laplace process, which together allow to obtain the clustering process. The clustering algorithm has been designed for online learning with different types of statistics and can be done efficiently. The clustering algorithm has been evaluated with several real-world bandits.
We propose a novel approach for automatic diagnosis of bipolar disorders based on the notion of a bipolar diagnosis. The method relies on a convolutional network to learn a multispectral model to predict whether a patient will show bipolar disorder based on data with three sources or not. The multispectral network model can perform a binary decision (to classify the patient as bipolar, or to classify a drug as non-bipolar) and can be implemented in an adversarial fashion. Experiments on both synthetic and real data demonstrate the superiority of the proposed method compared to state-of-the-art baselines.
We propose to model the causal relationships among events by using a framework with an adaptive structure. We first show the model is capable of predicting the outcome of the event based on the observed outcome. We then present a new nonparametric model to capture the causal relationship among multiple events based on the observed events. We demonstrate the model and demonstrate the effectiveness of the inference in several simulated and real events.
Fast Low-Rank Matrix Estimation for High-Dimensional Text Classification
A Nonparametric Coarse-Graining Approach to Image Denoising
An Efficient Stochastic Graph-based Clustering Scheme for Online Learning of Sparse Clustered Event Representations
Measures of Language Construction: A System for Spelling Correction of English and Dutch Papers
Deep Predicting Adolescent Suicide Attempts by Exploiting Drug-Drug InteractionsWe propose a novel approach for automatic diagnosis of bipolar disorders based on the notion of a bipolar diagnosis. The method relies on a convolutional network to learn a multispectral model to predict whether a patient will show bipolar disorder based on data with three sources or not. The multispectral network model can perform a binary decision (to classify the patient as bipolar, or to classify a drug as non-bipolar) and can be implemented in an adversarial fashion. Experiments on both synthetic and real data demonstrate the superiority of the proposed method compared to state-of-the-art baselines.
We propose to model the causal relationships among events by using a framework with an adaptive structure. We first show the model is capable of predicting the outcome of the event based on the observed outcome. We then present a new nonparametric model to capture the causal relationship among multiple events based on the observed events. We demonstrate the model and demonstrate the effectiveness of the inference in several simulated and real events.
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