We propose a multiresolution Gaussian process to capture long-range, non-Markovian dependencies while allowing for abrupt changes. The multiresolution GP hierarchically couples a collection of smooth GPs, each defined over an element of a random nested partition. Long-range dependencies are captured by the top-level GP…
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48 results for “GPS records”
The goal of this thesis is to investigate the potential of predictive modelling for football injuries. This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation of Wolverhampton Wanderers (WW). Three investigations were conducted: 1. Predicting the rec…
Enhanced framework predicts transportation modes from GPS data.
problem Predicting transportation modes from GPS records.
method Extended Etemad et al. framework, investigated wrapper search and information retrieval methods for feature selection, compared with deep learning methods.
result Framework achieved better performance than related papers.
cvHM framework speeds up GP inference for neural spike train analysis.
problem Scalability issue in approximate inference for latent GP models.
method cvHM framework using Hida-Matérn kernels and conjugate computation variational inference (CVI).
result Linear time inference for latent neural trajectories.
RVGP learns vector fields over unknown manifolds, preserving singularities.
problem Learning vector fields over unknown non-Euclidean manifolds.
method RVGP uses positional encoding with eigenfunctions of the connection Laplacian.
result RVGP preserves singularities in vector fields over unknown manifolds.
Generative Bayesian Computation improves surrogates for expensive simulations.
problem Limitations of Gaussian process surrogates in handling complex, non-stationary data.
method Generative Bayesian Computation via Implicit Quantile Networks (IQNs).
result Generative Bayesian Computation outperforms traditional Gaussian process methods across various benchmarks.
First private Bayesian optimization algorithm with provable performance.
problem Privacy-preserving Bayesian optimization in outsourced settings.
method Random projection-based transformation to preserve privacy and distances.
result Regret bound similar to non-private GP-UCB established for PO-GP-UCB.
Paper analyzes GPS data to identify POIs and user similarities.
problem Analyzing GPS data for meaningful places and user profiles.
method Data mining algorithms applied to raw GPS data.
result Steps to identify POIs and user similarities are satisfactory.
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Modelsstat.ML
Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have been applied to both regression and non-linear dimensionality reduction, and offer desirable properties such as uncertainty estimates, robustness to over-fitting, and principled ways for tuning hyper-parameters. However t…
Gaussian Processes (GPs) are widely used tools in statistics, machine learning, robotics, computer vision, and scientific computation. However, despite their popularity, they can be difficult to apply; all but the simplest classification or regression applications require specification and inference over complex covari…
CNNs become Gaussian processes with many filters, achieving state-of-the-art performance.
problem Training deep CNNs is computationally expensive.
method Showed that CNNs with many filters can be approximated by Gaussian processes, and computed the equivalent kernel efficiently.
result The kernel equivalent of a 32-layer ResNet achieves 0.84% classification error on MNIST.
Bayesian nonparametric method segments multi-sequence time series data.
problem Temporal segmentation of multi-sequence time series data into stationary segments.
method Gaussian process priors and nonparametric distribution for segment partitioning.
result Model effectively segments synthetic and real-time series data.
Paper proposes a new method to handle missing data in medical records using sequential variational autoencoders.
problem Missing data in medical records due to sensor off-times and uneven data collection.
method Sequential variational autoencoders (VAEs) with a new methodology called Shi-VAE.
result Shi-VAE achieves the best performance in terms of both metrics compared to state-of-the-art methods.
Automatic music transcription (AMT) aims to infer a latent symbolic representation of a piece of music (piano-roll), given a corresponding observed audio recording. Transcribing polyphonic music (when multiple notes are played simultaneously) is a challenging problem, due to highly structured overlapping between harmon…
A new model predicts discrete events with flexible, nonparametric baseline and excitation.
problem Limited flexibility in discrete Hawkes models for event prediction.
method Gaussian Process Discrete Hawkes Process (GP-DHP) with collapsed latent representation.
result Improves predictive log-likelihood for diverse event patterns.
GP-PCA reduces infinite-dimensional GP posteriors to a finite space for meta-learning.
problem How to define a structure for a set of Gaussian process posteriors.
method Information geometric framework and variational inference.
result GP-PCA improves meta-learning performance through reduced GP posteriors.
Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of d…
Two algorithms improve GP bandits by selecting priors and minimizing regret.
problem Selecting appropriate GP priors for unknown functions.
method Developed two algorithms: Prior-Elimination GP-TS and HyperPrior GP-TS.
result Established sublinear regret bound for HyperPrior GP-TS.
We introduce a framework for analyzing transductive combination of Gaussian process (GP) experts, where independently trained GP experts are combined in a way that depends on test point location, in order to scale GPs to big data. The framework provides some theoretical justification for the generalized product of GP e…
We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (…
Efficiently optimize GPs by reusing candidate solutions multiple times.
problem High computational cost of Gaussian process optimization due to unique historical points.
method Sticking to a candidate solution for multiple evaluation steps and limiting switches.
result Improved efficiency and practicality of Gaussian process optimization algorithms.
GP+ is a Python library for Gaussian process learning.
problem Probabilistic learning and inference for complex models.
method Integration of nonlinear manifold learning with Gaussian processes.
result GP+ enables probabilistic data fusion and inverse parameter estimation.
HIP-GP improves GP inference for inter-domain observations with millions of inducing points.
problem Inference for Gaussian Processes across different domains.
method Hierarchical inducing point Gaussian process with grid structure and stationary kernel assumption.
result Improved approximation accuracy through increased number of inducing points.
Central to robot exploration and mapping is the task of persistent localization in environmental fields characterized by spatially correlated measurements. This paper presents a Gaussian process localization (GP-Localize) algorithm that, in contrast to existing works, can exploit the spatially correlated field measurem…
This paper improves GP for learning complex data distributions.
problem Vanilla Gaussian processes struggle with complex data distributions.
method Introduces scalable GP paradigms with latent variables and variational inference.
result Scalable modulated GPs, especially latent GPs, learn diverse data distributions better.
Develops SGP-VAE for efficient sparse GP inference in multi-dimensional datasets.
problem Sparse GP approximations and missing data in multi-dimensional spatio-temporal datasets.
method Leverages partial inference networks for sparse GP approximations and amortized variational inference.
result Outperforms multi-output GPs and structured VAEs in various experiments.
Framework for applying GPs to real-world data with scalability guidelines.
problem Deployment of Gaussian Processes (GPs) is hindered by computational costs and lack of guidelines.
method Proposed a framework for identifying GP suitability and setting up robust models, formalizing decisions of experienced practitioners.
result More accurate results at test time for glacier elevation change case study.
It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width. This correspondence enables exact Bayesian inference for infinite width neural networks on regression tasks by means of eva…
We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models. We build on previous scalable GP research including stochastic variational inference based on inducing inputs, kernel interpolation, and structure exploiting algebra. The key idea of our method is to use Tensor Train decomposition fo…
Efficient sparse GP model improves audio source separation.
problem Sparse Gaussian Process (GP) inference is computationally expensive for long audio frames.
method Used GP regression, spectral mixture kernels, and variational sparse GPs.
result Proposed method outperforms LD-PSDTF, KL-NMF, and IS-NMF.
The paper defines conditions for Gaussian process sample path regularity.
problem Lack of understanding of Gaussian process sample path regularity.
method Analyzes covariance kernels to determine sample path regularity.
result Necessary and sufficient conditions for Hölder regularity are provided.
Extends Gaussian process regression for non-Gaussian data.
problem Inadequate modeling of uncertainty and over-smoothing in non-Gaussian datasets.
method Time-changed Gaussian processes with Lévy processes.
result Improved modeling of heavy-tailed non-Gaussian behaviors.
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due to its cubic time cost in the data size. This paper presents two parallel GP regression methods that exploit low-rank co…
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due to its cubic time cost in the data size. This paper presents two parallel GP regression methods that exploit low-rank co…
GP-DRF model handles variable-sized input and learns deep features.
problem Scaling deep Gaussian processes for variable-sized data.
method GP-DRF model combining GPs and DRF layers for efficient inference.
result GP-DRF outperforms standard GP and DRF models across various datasets.
A new method for faster prediction in distributed Gaussian processes.
problem Inefficient aggregation of distributed Gaussian processes with correlations.
method Proposes a novel approach for aggregated prediction in distributed GPs that incorporates correlations among experts.
result Results in more stable predictions in less time compared to state-of-the-art methods.
GP-KAN uses Gaussian Processes in KANs for robust, parameter-efficient non-linear modeling.
problem Non-linear modeling with limited parameters and uncertainty estimates.
method Integrates Gaussian Processes into Kolmogorov Arnold Networks (KANs) for robust non-linear modeling.
result GP-KAN achieves 98.5% accuracy on MNIST with 80k parameters compared to 1.5M for state-of-the-art models.
In the scenario of real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab tests is essential to enable successful medical interventions and improve patient outcomes. Developing a computational framework that can learn…
This paper analyzes regret bounds for Gaussian process Thompson sampling.
problem Analyzing the performance of Gaussian process Thompson sampling (GP-TS) in Bayesian optimization.
method The paper derives several regret bounds for GP-TS, including a lower bound, upper bounds on the second moment of cumulative regret, expected lenient regret, and improved cumulative regret.
result The paper provides improved regret upper bounds for GP-TS, showing that it suffers from a polynomial dependence on with probability .
GP-ALPS automatically selects latent processes for multi-output GPs.
problem Manual selection of latent processes in multi-output GPs is time-consuming and prone to biases.
method Developed a variational inference scheme to automatically choose latent processes.
result Demonstrated suitability of GP-ALPS in preliminary experiments.
We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel interpolation. The SKI framework clarifies how the quality of an inducing point a…
Deep Gaussian Processesstat.ML
In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP …
GP-CNAS uses genetic programming to automatically design CNN architectures.
problem Designing optimal CNN architectures is laborious and error-prone.
method GP-CNAS uses a tree-based representation of CNNs and dynamic crossover operators to search for optimal architectures.
result GP-CNAS finds optimal CNN architectures with balanced depth and width in limited trials.
Smooths GPS data with splines for noisy, irregularly sampled data.
problem Noisy, irregularly sampled GPS data with non-Gaussian noise.
method Smoothing splines with chosen spline order and tension parameter, allowing for non-Gaussian noise and outliers.
result Effective smoothing and interpolation of GPS data.
IE-GP framework learns sequentially arriving data with adaptive kernels.
problem Sequential data adaptation in Gaussian process models.
method Incremental ensemble of Gaussian process learners with adaptive weights.
result IE-GP framework effectively learns and adapts to sequential data.
This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and to states represented as a weighted sum of Gaussians. The key attr…
Unified view of GP approximations improves efficiency.
problem Disparate variational features limit GP efficiency.
method View GP as a Banach space to unify feature selection.
result Unified understanding of existing and new features.
Develops a method to model neural dynamics with flexible yet interpretable latent states.
problem Capturing complex nonlinear dynamics in neural time series while maintaining interpretability.
method Gaussian Process Switching Linear Dynamical System (gpSLDS) that balances expressiveness and interpretability.
result Favorable performance in comparison to rSLDS on synthetic and real neuroscience data.