Topic Tag: Bayes

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 Dynamic classifier chains for multi-label learning

In this paper, we deal with the task of building a dynamic ensemble of chain classifiers for multi-label classification. To do so, we proposed two concepts of classifier chains algorithms that are able to change label order of the chain without rebuilding the entire model. Such modes allows anticip…


 Bayesian Group Decisions: Algorithms and Complexity

We address the computations that Bayesian agents undertake to realize their optimal actions, as they repeatedly observe each other’s actions, following an initial private observation. We use iterated eliminations of infeasible signals (IEIS) to model the thinking process as well as the calcul…


 Informational Neurobayesian Approach to Neural Networks Training. Opportunities and Prospects

 

A study of the classification problem in context of information theory is presented in the paper. Current research in that field is focused on optimisation and bayesian approach. Although that gives satisfying results, they require a vast amount of data and computations to train on. Authors propose…


 Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems

 

Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We study in these models a decomposition of predictive uncertainty into its epistemic and aleatoric components. We show how such a decomposition a…


 A Bayesian Nonparametric Method for Clustering Imputation, and Forecasting in Multivariate Time Series

 

This article proposes a Bayesian nonparametric method for forecasting, imputation, and clustering in sparsely observed, multivariate time series. The method is appropriate for jointly modeling hundreds of time series with widely varying, non-stationary dynamics. Given a collection of $N$ time serie…


 Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning

 

In order for robots to perform mission-critical tasks, it is essential that they are able to quickly adapt to changes in their environment as well as to injuries and or other bodily changes. Deep reinforcement learning has been shown to be successful in training robot control policies for operation…


 Understanding Generalization and Stochastic Gradient Descent

 

This paper tackles two related questions at the heart of machine learning; how can we predict if a minimum will generalize to the test set, and why does stochastic gradient descent find minima that generalize well? Our work is inspired by Zhang et al. (2017), who showed deep networks can easily mem…


 Learning to Transfer Initializations for Bayesian Hyperparameter Optimization

   

Hyperparameter optimization undergoes extensive evaluations of validation errors in order to find the best configuration of hyperparameters. Bayesian optimization is now popular for hyperparameter optimization, since it reduces the number of validation error evaluations required. Suppose that we ar…


 Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect

We introduce a novel generative model for interpretable subgroup analysis for causal inference applications, Causal Rule Sets (CRS). A CRS model uses a small set of short rules to capture a subgroup where the average treatment effect is elevated compared to the entire population. We present a Bayes…


 Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks

 

Minimizing non-convex and high-dimensional objective functions is challenging, especially when training modern deep neural networks. In this paper, a novel approach is proposed which divides the training process into two consecutive phases to obtain better generalization performance: Bayesian sampl…


 Bayesian Alignments of Warped Multi-Output Gaussian Processes

 

We present a Bayesian extension to convolution processes which defines a representation between multiple functions by an embedding in a shared latent space. The proposed model allows for both arbitrary alignments of the inputs and and also non-parametric output warpings to transform the observation…


 A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm

In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning (SBL). In particular, GGAMP is used to implement the E-step in SBL in pla…


 Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo

 

A key task in Bayesian statistics is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). However, without any assumptions, sampling (even approximately) can be #P-hard, and few works have provided “beyond worst-case” guaran…


 Learning Graphical Models from a Distributed Stream

A current challenge for data management systems is to support the construction and maintenance of machine learning models over data that is large, multi-dimensional, and evolving. While systems that could support these tasks are emerging, the need to scale to distributed, streaming data requires ne…


 Learning Scalable Deep Kernels with Recurrent Structure

     

Many applications in speech, robotics, finance, and biology deal with sequential data, where ordering matters and recurrent structures are common. However, this structure cannot be easily captured by standard kernel functions. To model such structure, we propose expressive closed-form kernel functi…


 Remote Sensing Image Classification with Large Scale Gaussian Processes

    

Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and…


 The Dutch’s Real World Financial Institute: Introducing Quantum-Like Bayesian Networks as an Alternative Model to deal with Uncertainty

In this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands. The log is robust in terms of data, containing a total of 262 200 event logs, belonging to 13 087 different credit applications. The dataset is heterogeneous and consists of a mi…


 The Dutch’s Real World Financial Institute: Introducing Quantum-Like Bayesian Networks as an Alternative Model to deal with Uncertainty

In this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands. The log is robust in terms of data, containing a total of 262 200 event logs, belonging to 13 087 different credit applications. The dataset is heterogeneous and consists of a mi…


 The Dutch’s Real World Financial Institute: Introducing Quantum-Like Bayesian Networks as an Alternative Model to deal with Uncertainty

In this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands. The log is robust in terms of data, containing a total of 262 200 event logs, belonging to 13 087 different credit applications. The dataset is heterogeneous and consists of a mi…


 Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization

 

In this paper we study the personalized text search problem. The keyword based search method in conventional algorithms has a low efficiency in understanding users’ intention since the semantic meaning, user profile, user interests are not always considered. Firstly, we propose a novel text s…


 Upper Bound of Bayesian Generalization Error in Non-negative Matrix Factorization

  

Non-negative matrix factorization (NMF) is a new knowledge discovery method that is used for text mining, signal processing, bioinformatics, and consumer analysis. However, its basic property as a learning machine is not yet clarified, as it is not a regular statistical model, resulting that theore…


 Bayesian estimation from few samples: community detection and related problems

We propose an efficient meta-algorithm for Bayesian estimation problems that is based on low-degree polynomials, semidefinite programming, and tensor decomposition. The algorithm is inspired by recent lower bound constructions for sum-of-squares and related to the method of moments. Our focus is on…


 Vision-based deep execution monitoring

  

Execution monitor of high-level robot actions can be effectively improved by visual monitoring the state of the world in terms of preconditions and postconditions that hold before and after the execution of an action. Furthermore a policy for searching where to look at, either for verifying the rel…


 Human motion primitive discovery and recognition

 

We present a novel framework for the automatic discovery and recognition of human motion primitives from motion capture data. Human motion primitives are discovered by optimizing the ‘motion flux’, a quantity which depends on the motion of a group of skeletal joints. Models of each prim…


 Distance-based Confidence Score for Neural Network Classifiers

 

The reliable measurement of confidence in classifiers’ predictions is very important for many applications and is, therefore, an important part of classifier design. Yet, although deep learning has received tremendous attention in recent years, not much progress has been made in quantifying t…