A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
HIP-GP improves GP inference for inter-domain observations with millions of inducing points.
Bayesian estimators for causal inference using hierarchical Gaussian Processes.
Proposes GPHMEs using Gaussian processes for hierarchical expert models.
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…
The study optimizes Gaussian process approximations for finite-rank models.
Bayesian framework reduces high-dimensional GP modeling costs.
MPHD transfers knowledge across different domains for Bayesian optimization.
HierGP improves emulator efficiency for sparse, structured data.
The paper predicts an Efficient Market Property for the equity market, where stocks, when denominated in units of the growth optimal portfolio (GP), have zero instantaneous expected returns. Well-diversified equity portfolios are shown to approximate the GP, which explains the well-observed good performance of equally …
In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. We design an inference algorithm based on Expectation Propagation and evaluate the mode…
Unified view on GP transfer learning for Bayesian optimization.
Standard Gaussian Process (GP) regression, a powerful machine learning tool, is computationally expensive when it is applied to large datasets, and potentially inaccurate when data points are sparsely distributed in a high-dimensional feature space. To address these challenges, a new multiscale, sparsified GP algorithm…
ScaML-GP efficiently learns from few meta-tasks using Gaussian processes.
Focalized GP improves Bayesian optimization for large datasets.
Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation within the hierarchy. Recently, [1] pointed out that the hierarchical s…
Efficiently trains deep Gaussian processes on large datasets.
Wide neural networks can degrade performance, contrary to conventional wisdom.
MTL-NAS combines NAS with GP-MTL for task-agnostic multi-task learning.
Gaussian processes (GP) are powerful tools for probabilistic modeling purposes. They can be used to define prior distributions over latent functions in hierarchical Bayesian models. The prior over functions is defined implicitly by the mean and covariance function, which determine the smoothness and variability of the …
Sparse GPs improved with nearest neighbor inducing variables.
Gaussian Processes improve missing value imputation in datasets.
This paper reviews recent advances in Gaussian process regression methods.
Combines physics-based ML with hierarchical Bayesian techniques for better model performance.
Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussian Processes (GP) that have proven to work effectively on a multiple supervised regression tasks. They combine the well calibrated uncertainty estimates of GPs with the great flexibility of multilayer models. In DGPs, given the inputs, the outputs …
Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but scaling GPs to lar…
Multi-output Gaussian processes (GPs) are a flexible Bayesian nonparametric framework that has proven useful in jointly modeling the physiological states of patients in medical time series data. However, capturing the short-term effects of drugs and therapeutic interventions on patient physiological state remains chall…
We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-expo…
New models improve stock and wind speed forecasting.
This work evaluates uncertainty in deep Gaussian processes.
GPflux simplifies deep Gaussian processes for Python.
The paper develops a state-space approach to deep Gaussian processes for efficient state estimation.
A new model combines deep learning and Gaussian Processes with hyperdata learning.
A large GPS dataset reveals that a single route often covers 60% of travel observations.
Deep Gaussian processes reduce uncertainty in porous media flow modeling.
Uncertainty quantification has been a core of the statistical machine learning, but its computational bottleneck has been a serious challenge for both Bayesians and frequentists. We propose a model-based framework in quantifying uncertainty, called predictive-matching Generative Parameter Sampler (GPS). This procedure …
Paper uses Gaussian processes to handle shared latent confounders in causal inference.
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…
Efficiently clusters data with weak assumptions, robust to contamination.
The paper extends NSGPs with -regularization for sparsity and solves the resulting R-NSGP regression problem.
Capsule Networks attempt to represent patterns in images in a way that preserves hierarchical spatial relationships. Additionally, research has demonstrated that these techniques may be robust against adversarial perturbations. We present an improvement to training capsule networks with added robustness via non-paramet…
DeepICMGP surrogate models multiple outputs efficiently.
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are nonparametric probabilistic models and as such are arguably more flexible, have a greater capacity to generalise, …
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are probabilistic and non-parametric and as such are arguably more flexible, have a greater capacity to generalise, an…
The paper provides theoretical guarantees for transformation-based models in variational inference.
Deep Transformed Gaussian Processes extend TGPs with variational inference for scalable multi-layer modeling.
In this paper, the problem of estimating the level set of a black-box function from noisy and expensive evaluation queries is considered. A new algorithm for this problem in the Bayesian framework with a Gaussian Process (GP) prior is proposed. The proposed algorithm employs a hierarchical sequence of partitions to exp…
Develops scalable model for learning velocity fields in complex traffic scenarios.
The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it…