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Copulas are a powerful tool for modeling multivariate distributions as they allow to separately estimate the univariate marginal distributions and the joint dependency structure. However, known parametric copulas offer limited flexibility…

Machine Learning · Statistics 2021-11-11 Tim Janke , Mohamed Ghanmi , Florian Steinke

Stein's method (Stein, 1973; 1981) is a powerful tool for statistical applications and has significantly impacted machine learning. Stein's lemma plays an essential role in Stein's method. Previous applications of Stein's lemma either…

Machine Learning · Statistics 2025-02-04 Wu Lin , Mohammad Emtiyaz Khan , Mark Schmidt

In this survey, symmetry provides a framework for classification of manifolds with differential-geometric structures. We highlight pseudo-Riemannian metrics, conformal structures, and projective structures. A range of techniques have been…

Differential Geometry · Mathematics 2020-09-30 Karin Melnick

Differentiable rendering aims to compute the derivative of the image rendering function with respect to the rendering parameters. This paper presents a novel algorithm for 6-DoF pose estimation through gradient-based optimization using a…

Computer Vision and Pattern Recognition · Computer Science 2022-12-27 Ramchander Rao Bhaskara , Roshan Thomas Eapen , Manoranjan Majji

Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are distributed across the nodes of the network. We propose a general approach for distributed learning based on…

Machine Learning · Computer Science 2012-07-03 Qiang Liu , Alexander Ihler

We generalize several important results from the perturbation theory of linear operators to the setting of semisimple orthogonal symmetric Lie algebras. These Lie algebras provide a unifying framework for various notions of matrix…

Representation Theory · Mathematics 2023-07-04 Emanuel Malvetti , Gunther Dirr , Frederik vom Ende , Thomas Schulte-Herbrüggen

Recently, combinations of generative and Bayesian machine learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated…

Machine Learning · Computer Science 2024-11-21 Sebastian Bieringer , Sascha Diefenbacher , Gregor Kasieczka , Mathias Trabs

Models with intractable likelihood functions arise in areas including network analysis and spatial statistics, especially those involving Gibbs random fields. Posterior parameter es timation in these settings is termed a doubly-intractable…

Computation · Statistics 2018-10-16 Lampros Bouranis , Nial Friel , Florian Maire

Here we present a new non-parametric approach to density estimation and classification derived from theory in Radon transforms and image reconstruction. We start by constructing a "forward problem" in which the unknown density is mapped to…

Numerical Analysis · Mathematics 2024-12-20 James Webber , Erika Hussey , Eric Miller , Shuchin Aeron

Lie theory is, beyond any doubt, an absolutely essential part of differential geometry. It is therefore necessary to seek its generalization to $\mathbb{Z}$-graded geometry. In particular, it is vital to construct non-trivial and explicit…

Differential Geometry · Mathematics 2025-11-10 Jan Vysoky

It is now practically the norm for data to be very high dimensional in areas such as genetics, machine vision, image analysis and many others. When analyzing such data, parametric models are often too inflexible while nonparametric…

Methodology · Statistics 2011-05-31 Abhishek Bhattacharya , Garritt Page , David Dunson

Large kernel convolutions offer a scalable alternative to vision transformers for high-resolution 3D volumetric analysis, yet naively increasing kernel size often leads to optimization instability. Motivated by the spatial bias inherent in…

Computer Vision and Pattern Recognition · Computer Science 2026-02-02 Ho Hin Lee , Quan Liu , Shunxing Bao , Yuankai Huo , Bennett A. Landman

Reconstructing three-dimensional (3D) scenes with semantic understanding is vital in many robotic applications. Robots need to identify which objects, along with their positions and shapes, to manipulate them precisely with given tasks.…

Robotics · Computer Science 2024-12-17 Khang Nguyen , Tuan Dang , Manfred Huber

Model reparametrization, which follows the change-of-variable rule of calculus, is a popular way to improve the training of neural nets. But it can also be problematic since it can induce inconsistencies in, e.g., Hessian-based flatness…

Machine Learning · Computer Science 2023-10-24 Agustinus Kristiadi , Felix Dangel , Philipp Hennig

Low rank inference on matrices is widely conducted by optimizing a cost function augmented with a penalty proportional to the nuclear norm $\Vert \cdot \Vert_*$. However, despite the assortment of computational methods for such problems,…

Machine Learning · Statistics 2025-10-08 Simon Segert , Nathan Wycoff

Modeling complex conditional distributions is critical in a variety of settings. Despite a long tradition of research into conditional density estimation, current methods employ either simple parametric forms or are difficult to learn in…

Machine Learning · Statistics 2018-02-15 Brian L Trippe , Richard E Turner

The linearization of complex ordinary differential equations is studied by extending Lie's criteria for linearizability to complex functions of complex variables. It is shown that the linearization of complex ordinary differential equations…

Classical Analysis and ODEs · Mathematics 2011-07-25 S. Ali , F. M. Mahomed , Asghar Qadir

We use the notion of the principal three-dimensional subgroup of a simple Lie group to identify certain special subspaces of the Lie algebra and address the question of whether these are calibrated for invariant forms on the group.

Differential Geometry · Mathematics 2022-01-19 Nigel Hitchin

Many machine learning tasks require sampling a subset of items from a collection based on a parameterized distribution. The Gumbel-softmax trick can be used to sample a single item, and allows for low-variance reparameterized gradients with…

Machine Learning · Computer Science 2021-03-02 Sang Michael Xie , Stefano Ermon

Normalizing flows are a popular class of models for approximating probability distributions. However, their invertible nature limits their ability to model target distributions whose support have a complex topological structure, such as…

Machine Learning · Statistics 2022-02-25 Vincent Stimper , Bernhard Schölkopf , José Miguel Hernández-Lobato
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