Topic Tag: Bayes

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 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…


 Lower Bounds on the Bayes Risk of the Bayesian BTL Model with Applications to Random Graphs

We consider the problem of aggregating pairwise comparisons to obtain a consensus ranking order over a collection of objects. We employ the popular Bradley-Terry-Luce (BTL) model in which each object is associated with a skill parameter which allows us to probabilistically describe the pairwise com…


 Bayesian Pool-based Active Learning With Abstention Feedbacks

We study pool-based active learning with abstention feedbacks, where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important problem with many useful applications. We take a Bayesian approach to the problem and develop two new greedy algorithms …


 Generative learning for deep networks

 

Learning, taking into account full distribution of the data, referred to as generative, is not feasible with deep neural networks (DNNs) because they model only the conditional distribution of the outputs given the inputs. Current solutions are either based on joint probability models facing diffic…


 Bayesian Filtering for ODEs with Bounded Derivatives

Recently there has been increasing interest in probabilistic solvers for ordinary differential equations (ODEs) that return full probability measures, instead of point estimates, over the solution and can incorporate uncertainty over the ODE at hand, e.g. if the vector field or the initial value is…


 Intrusions in Marked Renewal Processes

We present a probabilistic model of an intrusion in a marked renewal process. Given a process and a sequence of events, an intrusion is a subsequence of events that is not produced by the process. Applications of the model are, for example, online payment fraud with the fraudster taking over a user…


 GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs

  

This work centers on the problem of stochastic filtering for systems that yield complex beliefs. The main contribution is GP-SUM, a filtering algorithm for dynamic systems expressed as Gaussian Processes (GP), that does not rely on linearizations or Gaussian approximations of the belief. The algori…


 Robust Probabilistic Modeling with Bayesian Data Reweighting

Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust models offer protection against mismatch between a model’s assumptions and reality. We propose a way to systematical…


 Bayesian Optimization for Parameter Tuning of the XOR Neural Network

 

When applying Machine Learning techniques to problems, one must select model parameters to ensure that the system converges but also does not become stuck at the objective function’s local minimum. Tuning these parameters becomes a non-trivial task for large models and it is not always appare…


 Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale

We present a scalable and robust Bayesian inference method for linear state space models. The method is applied to demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson…


 Goal-Driven Dynamics Learning via Bayesian Optimization

Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics. Therefore, it might be desirable to take a task-specific approach, wherein the focus is on explicitly learning the dynamics…


 Defining a Lingua Franca to Open the Black Box of a Naïve Bayes Recommender

Many AI systems have a black box nature that makes it difficult to understand how they make their recommendations. This can be unsettling, as the designer cannot be certain how the system will respond to novelty. To penetrate our Na”ive Bayes recommender’s black box, we first asked, wha…


 A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering

 

We propose an effective method to solve the event sequence clustering problems based on a novel Dirichlet mixture model of a special but significant type of point processes — Hawkes process. In this model, each event sequence belonging to a cluster is generated via the same Hawkes process wit…


 On the Design of LQR Kernels for Efficient Controller Learning

  

Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on…


 Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search

One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e.g., from simulation or from previous tasks) to accelerate learning on a robot. In this paper, we are interested in situations for which several priors exist but we do not know in…


 Analogical-based Bayesian Optimization

  

Some real-world problems revolve to solve the optimization problem max_{xinmathcal{X}}fleft(xright) where fleft(.right) is a black-box function and X might be the set of non-vectorial objects (e.g., distributions) where we can only define a symmetric and non-negative similarity score on it. This se…