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Stochastic Gradient Descent (SGD) is an important algorithm in machine learning. With constant learning rates, it is a stochastic process that, after an initial phase of convergence, generates samples from a stationary distribution. We show…

Machine Learning · Statistics 2017-09-12 Stephan Mandt , Matthew D. Hoffman , David M. Blei

Quantum generative models use the intrinsic probabilistic nature of quantum mechanics to learn and reproduce complex probability distributions. In this paper, we present an implementation of a 3-qubit quantum circuit Born machine trained to…

Quantum Physics · Physics 2025-08-04 Soumyadip Sarkar

Gaussian Process Regression (GPR) is a nonparametric supervised learning method, widely valued for its ability to quantify uncertainty. Despite its advantages and broad applications, classical GPR implementations face significant…

Quantum Physics · Physics 2025-03-25 Junpeng Hu , Jinglai Li , Lei Zhang , Shi Jin

A hybrid quantum-classical method for learning Boltzmann machines (BM) for a generative and discriminative task is presented. Boltzmann machines are undirected graphs with a network of visible and hidden nodes where the former is used as…

Quantum Physics · Physics 2022-07-21 Siddhartha Srivastava , Veera Sundararaghavan

An exciting branch of machine learning research focuses on methods for learning, optimizing, and integrating unknown functions that are difficult or costly to evaluate. A popular Bayesian approach to this problem uses a Gaussian process…

Machine Learning · Statistics 2018-04-02 Anqi Wu , Mikio C. Aoi , Jonathan W. Pillow

A boson sampling device is a specialised quantum computer that solves a problem which is strongly believed to be computationally hard for classical computers. Recently a number of small-scale implementations have been reported, all based on…

Recently, 3D Gaussian Splatting has emerged as a promising approach for modeling 3D scenes using mixtures of Gaussians. The predominant optimization method for these models relies on backpropagating gradients through a differentiable…

Computer Vision and Pattern Recognition · Computer Science 2025-09-24 Toon Van de Maele , Ozan Catal , Alexander Tschantz , Christopher L. Buckley , Tim Verbelen

This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose approximations are variationally optimal with respect to the full-rank GPR model enriched…

Machine Learning · Computer Science 2019-03-25 Haibin Yu , Trong Nghia Hoang , Kian Hsiang Low , Patrick Jaillet

Granular ball computing (GBC), as an efficient, robust, and scalable learning method, has become a popular research topic of granular computing. GBC includes two stages: granular ball generation (GBG) and multi-granularity learning based on…

Machine Learning · Computer Science 2025-04-10 Qin Xie , Qinghua Zhang , Shuyin Xia , Fan Zhao , Chengying Wu , Guoyin Wang , Weiping Ding

Sampling-based algorithms are classical approaches to perform Bayesian inference in inverse problems. They provide estimators with the associated credibility intervals to quantify the uncertainty on the estimators. Although these methods…

Methodology · Statistics 2023-11-28 Pierre-Antoine Thouvenin , Audrey Repetti , Pierre Chainais

In the realm of statistical learning, the increasing volume of accessible data and increasing model complexity necessitate robust methodologies. This paper explores two branches of robust Bayesian methods in response to this trend. The…

Methodology · Statistics 2024-12-02 Masahiro Tanaka

To accurately reproduce measurements from the real world, simulators need to have an adequate model of the physical system and require the parameters of the model be identified. We address the latter problem of estimating parameters through…

Robotics · Computer Science 2022-03-01 Eric Heiden , Christopher E. Denniston , David Millard , Fabio Ramos , Gaurav S. Sukhatme

Humans are able to accelerate their learning by selecting training materials that are the most informative and at the appropriate level of difficulty. We propose a framework for distributing deep learning in which one set of workers search…

Machine Learning · Statistics 2016-04-19 Guillaume Alain , Alex Lamb , Chinnadhurai Sankar , Aaron Courville , Yoshua Bengio

Random point patterns are ubiquitous in nature, and statistical models such as point processes, i.e., algorithms that generate stochastic collections of points, are commonly used to simulate and interpret them. We propose an application of…

Quantum Physics · Physics 2020-03-04 Soran Jahangiri , Juan Miguel Arrazola , Nicolás Quesada , Nathan Killoran

Gaussian Process (GP) regression is a popular and sample-efficient approach for many engineering applications, where observations are expensive to acquire, and is also a central ingredient of Bayesian optimization (BO), a highly prevailing…

Machine Learning · Statistics 2025-06-06 Sébastien Da Veiga

Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using quantum kernels based on parameterized quantum circuits. By employing a hardware-efficient…

Quantum Physics · Physics 2024-02-06 Frederic Rapp , Marco Roth

The Poisson model is frequently employed to describe count data, but in a Bayesian context it leads to an analytically intractable posterior probability distribution. In this work, we analyze a variational Gaussian approximation to the…

Numerical Analysis · Mathematics 2018-02-14 Simon Arridge , Kazufumi Ito , Bangti Jin , Chen Zhang

Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We propose to transfer information across tasks using learnt…

Machine Learning · Statistics 2019-05-28 Ho Chung Leon Law , Peilin Zhao , Lucian Chan , Junzhou Huang , Dino Sejdinovic

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,…

Machine Learning · Statistics 2014-10-01 Yarin Gal , Mark van der Wilk , Carl E. Rasmussen

Gaussian building blocks are essential for photonic quantum information processing, and universality can be practically achieved by equipping Gaussian circuits with adaptive measurement and feedforward. The number of adaptive steps then…

Quantum Physics · Physics 2026-02-17 Changhun Oh , Youngrong Lim