arXiv research

Study on discrete Gaussian curvature for polyhedral surfaces.

Study of Ricci flow on discrete surfaces of revolution with constant Gaussian curvature.

New method approximates Gaussian curvature on discrete surfaces.

The paper introduces a new discretization of Gaussian curvature on surfaces.

Discrete Gaussian noise preserves privacy and accuracy in differential privacy.

A system for federated learning with private data, adding discrete Gaussian noise and secure aggregation.

The paper proves a theorem for discretizing Gaussian curvature on surfaces.

This article is an application of the author's paper about a construction method for discrete constant negative Gaussian curvature surfaces, the nonlinear d'Alembert formula. The heart of this formula is the Birkhoff decomposition, and we give a simple algorithm for the Birkhoff decomposition. As an application, we dra…

This work develops discrete Gaussian models for vector-valued data on triangular meshes.

A new RG approach connects discrete and continuous time descriptions of Gaussian processes.

RL solves discrete LQ control with Gaussian optimal policy.

Study discrete analog of zeta-determinant maximization on triangulated surfaces.

Paper solves NGCA for discrete distributions using LLL method.

The paper tackles prescribing discrete Gaussian curvature on polyhedral surfaces.

New Bäcklund transformations for discrete pseudospherical surfaces of revolution are found.

A new method for computing image curvature efficiently and accurately.

A new model predicts discrete events with flexible, nonparametric baseline and excitation.

Study of discrete-time mean-variance model using reinforcement learning.

The paper establishes a discrete uniformization theorem for surfaces with piecewise hyperbolic metrics.

Estimating causal models from observational data is a crucial task in data analysis. For continuous-valued data, Shimizu et al. have proposed a linear acyclic non-Gaussian model to understand the data generating process, and have shown that their model is identifiable when the number of data is sufficiently large. Howe…

The paper is concerned with non-linear Gaussian filtering and smoothing in continuous-discrete state-space models, where the dynamic model is formulated as an Itô stochastic differential equation (SDE), and the measurements are obtained at discrete time instants. We propose novel Taylor moment expansion (TME) Gaussian …

The paper finds circle packings with specific curvatures in hyperbolic geometry.

PDHAMS improves sampling for discrete distributions with quadratic potential functions.

New method learns disentangled discrete representations using categorical variational autoencoders.

Ada-BKB optimizes black-box functions on continuous domains with adaptive discretization.

New geometric SDEs and discretizations on Riemannian manifolds with error bounds.

Paper develops Bayesian inference for discrete-choice mnp models with Gaussian priors.

Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel methods like Support Vector Machines may offer good predictive performances but do not intrinsically provide uncertainty estimates. In contras…

PAGP uses physics-assisted Gaussian processes to solve and learn PDEs.

The paper improves privacy accounting for discrete-valued mechanisms and the subsampled Gaussian mechanism.

A new method scales Gaussian process variational autoencoders to handle high-dimensional time series.

Library learns Bayesian networks from mixed data without discretization.

In this paper we define and analyze singularities of discrete linear Weingarten surfaces with Weierstrass-type representations in $3$-dimensional Riemannian and Lorentzian spaceforms. In particular, we discuss singularities of discrete surfaces with non-zero constant Gaussian curvature, and parallel surfaces of discret…

Simplified analysis of diffusion models using discrete random variables.

BayesSum improves Bayesian quadrature for discrete domains, requiring fewer samples.

Paper formulates mutual information optimal control for discrete-time systems.

Bayesian optimization tackles mixed discrete-continuous problems with Gaussian processes.

Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalizat…

Unified analysis of Gaussian Process Thompson Sampling without discretization.

D2P-Fed improves privacy and communication in federated learning.

Safety filter for unknown discrete-time systems with learned models and noise covariance.

Random Gaussian fields on 4D Riemannian manifolds with conformal invariance.

Discond-VAE separates continuous and discrete factors in data.

Recently developed techniques have made it possible to quickly learn accurate probability density functions from data in low-dimensional continuous space. In particular, mixtures of Gaussians can be fitted to data very quickly using an accelerated EM algorithm that employs multiresolution kd-trees (Moore, 1999). In thi…

We study how the round-off (or discretization) error changes the statistical properties of a Gaussian long memory process. We show that the autocovariance and the spectral density of the discretized process are asymptotically rescaled by a factor smaller than one, and we compute exactly this scaling factor. Consequentl…

We use the explicit relation between genus filtrated $s$-loop means of the Gaussian matrix model and terms of the genus expansion of the Kontsevich--Penner matrix model (KPMM), which is the generating function for volumes of discretized (open) moduli spaces $M_{g,s}^{disc}$ (discrete volumes), to express Gaussian means…

A novel multi-resolution Gaussian process model for efficient time traversal.

We explore a new method for discrete-time control problems using randomization and entropy.