arXiv research

This paper shows how forward rate interpolations are equivalent to discount factor interpolations in yield curve construction.

SoftKI combines SKI and variational methods for scalable GP regression.

In sparse-view Computed Tomography (CT), only a small number of projection images are taken around the object, and sinogram interpolation method has a significant impact on final image quality. When the amount of sparsity (the amount of missing views in sinogram data) is not high, conventional interpolation methods hav…

Paper investigates optimal interpolation methods in linear regression.

Bayesian interpolants explain neural network inferences concisely.

In this paper, we propose a method to learn a minimizing geodesic within a data manifold. Along the learned geodesic, our method can generate high-quality interpolations between two given data samples. Specifically, we use an autoencoder network to map data samples into latent space and perform interpolation via an int…

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…

The study examines how different interpolation methods affect the decomposition of life insurance surplus.

New approach uses interpolation models and error bounds for verifiable scientific machine learning.

A physics-based method improves data interpolators and regression tasks.

Exploiting the deep generative model's remarkable ability of learning the data-manifold structure, some recent researches proposed a geometric data interpolation method based on the geodesic curves on the learned data-manifold. However, this interpolation method often gives poor results due to a topological difference …

This research solves Hermite interpolation on manifolds using retractions.

Deep neural networks can interpolate any dataset in the overparametrized regime.

Enhances interpolation paths in latent space using particle filters.

REPAIR mitigates variance collapse to enable linear interpolation between SGD solutions.

We focus on an interpolation method referred to Bayesian reconstruction in this paper. Whereas in standard interpolation methods missing data are interpolated deterministically, in Bayesian reconstruction, missing data are interpolated probabilistically using a Bayesian treatment. In this paper, we address the framewor…

New optimization method helps models generalize better after achieving near-perfect training performance.

New insights into Nadaraya-Watson interpolators show varied generalization behaviors.

Develops interpolation methods for matrix functions in statistics and machine learning.

The study finds flaws in methods used to estimate foreign exchange option prices.

Piecewise polynomial interpolation-based gradient descent reduces oracle complexity for smooth loss functions.

The paper proves approximation and interpolation theorems for maxfaces with singularities.

GPMI method interpolates uncertain atrial conduction velocity on non-Euclidean manifolds.

In order to generate novel 3D shapes with machine learning, one must allow for interpolation. The typical approach for incorporating this creative process is to interpolate in a learned latent space so as to avoid the problem of generating unrealistic instances by exploiting the model's learned structure. The process o…

This paper introduces an interpolation-based method, called the reconstruction approach, for nonparametric regression. Based on the fact that interpolation usually has negligible errors compared to statistical estimation, the reconstruction approach uses an interpolator to parameterize the regression function with its …

Two methods for interpolating manifold-valued data are presented.

We show that learning methods interpolating the training data can achieve optimal rates for the problems of nonparametric regression and prediction with square loss.

DSoftKI scales GP regression with full derivative observations.

We show that stochastic interpolation flow maps are Lipschitz with a sharp constant.

A new image interpolation model using sparse representation and nonlocal linear regression.

The over-parameterized models attract much attention in the era of data science and deep learning. It is empirically observed that although these models, e.g. deep neural networks, over-fit the training data, they can still achieve small testing error, and sometimes even {\em outperform} traditional algorithms which ar…

New method interpolates high-dimensional scattered data using kernel theory.

A new interpolation method speeds up neural ODE training.

This work proposes a novel method for interpolating ROMs without solving FEM models.

Private optimization faster on interpolation problems with quadratic growth.

Paper shows faster convergence to local-minimizers in over-parametrized models under interpolation-like conditions.

New method interpolates training data and is consistent for various data distributions.

A new GP interpolation method for better predictive distributions in ranges of interest.

Interpolates curves using maximal and minimal surfaces in different spaces.

Paper introduces a method to explain deep learning models and identify good generalization.

FFRK automatically extracts features for spatial interpolation without external variables.

Gaussian Processes improve data interpolation from diverse experiments.

CNNs predict spatial fields from sparse data.

Improved complexity for machine learning optimization methods.

New sampling method uses stochastic interpolants and FBSDEs.

Study improves sugarcane plot prediction using data interpolation.

Neural networks learn incrementally from orthogonal data, interpolating with minimal complexity.

The paper connects flatness to generalization in learning multi-index models with neural networks.