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Bayesian optimization through Gaussian process regression is an effective method of optimizing an unknown function for which every measurement is expensive. It approximates the objective function and then recommends a new measurement point…

Machine Learning · Statistics 2017-05-17 Hildo Bijl , Thomas B. Schön , Jan-Willem van Wingerden , Michel Verhaegen

Biclustering is a problem in machine learning and data mining that seeks to group together rows and columns of a dataset according to certain criteria. In this work, we highlight the natural relation that quantum computing models like boson…

Quantum Physics · Physics 2024-05-30 Ajinkya Borle , Ameya Bhave

The Bayesian smoothing equations are generally intractable for systems described by nonlinear stochastic differential equations and discrete-time measurements. Gaussian approximations are a computationally efficient way to approximate the…

Dynamical Systems · Mathematics 2016-04-05 Juha Ala-Luhtala , Simo Särkkä , Robert Piché

While much research effort has been dedicated to scaling up sparse Gaussian process (GP) models based on inducing variables for big data, little attention is afforded to the other less explored class of low-rank GP approximations that…

Machine Learning · Statistics 2016-11-21 Quang Minh Hoang , Trong Nghia Hoang , Kian Hsiang Low

Gaussian processes (GP) provide a prior over functions and allow finding complex regularities in data. Gaussian processes are successfully used for classification/regression problems and dimensionality reduction. In this work we consider…

Machine Learning · Computer Science 2016-11-21 Pavel Izmailov , Dmitry Kropotov

Quantum computers can efficiently sample from probability distributions that are believed to be classically intractable, providing a foundation for quantum generative modeling. However, practical training of such models remains challenging,…

Quantum Physics · Physics 2025-11-18 Maria Demidik , Cenk Tüysüz , Michele Grossi , Karl Jansen

Efficient sampling from a classical Gibbs distribution is an important computational problem with applications ranging from statistical physics over Monte Carlo and optimization algorithms to machine learning. We introduce a family of…

Quantum Physics · Physics 2021-09-08 Dominik S. Wild , Dries Sels , Hannes Pichler , Cristian Zanoci , Mikhail D. Lukin

Gaussian processes are widely known for their ability to provide probabilistic predictions in supervised machine learning models. Their non-parametric nature and flexibility make them particularly effective for regression tasks. However,…

The use of Gaussian processes (GPs) is supported by efficient sampling algorithms, a rich methodological literature, and strong theoretical grounding. However, due to their prohibitive computation and storage demands, the use of exact GPs…

Statistics Theory · Mathematics 2022-07-27 Kelly R. Moran , Matthew W. Wheeler

Boson sampling has been theoretically proposed and experimentally demonstrated to show quantum computational advantages. However, it still lacks the deep understanding of the practical applications of boson sampling. Here we propose that…

Quantum Physics · Physics 2023-11-10 Wen-Qiang Liu , Zhang-qi Yin

Gaussian boson sampling is a promising candidate for showing experimental quantum advantage. While there is evidence that noiseless Gaussian boson sampling is hard to efficiently simulate using a classical computer, the current Gaussian…

Quantum Physics · Physics 2024-09-24 Changhun Oh , Minzhao Liu , Yuri Alexeev , Bill Fefferman , Liang Jiang

Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points. We explore an alternative approximation that employs stochastic inference…

Machine Learning · Statistics 2019-05-28 Jiaxin Shi , Mohammad Emtiyaz Khan , Jun Zhu

Learning dynamical models from data is not only fundamental but also holds great promise for advancing principle discovery, time-series prediction, and controller design. Among various approaches, Gaussian Process State-Space Models…

Machine Learning · Computer Science 2025-10-20 Tengjie Zheng , Haipeng Chen , Lin Cheng , Shengping Gong , Xu Huang

Optimized sensing is important for computational imaging in low-resource environments, when images must be recovered from severely limited measurements. In this paper, we propose a physics-constrained, fully differentiable, autoencoder that…

Image and Video Processing · Electrical Eng. & Systems 2020-03-24 He Sun , Adrian V. Dalca , Katherine L. Bouman

This work presents a novel realization approach to Quantum Boltzmann Machines (QBMs). The preparation of the required Gibbs states, as well as the evaluation of the loss function's analytic gradient is based on Variational Quantum Imaginary…

Quantum Physics · Physics 2021-03-01 Christa Zoufal , Aurélien Lucchi , Stefan Woerner

Gradient Boosting Machine (GBM) introduced by Friedman is a powerful supervised learning algorithm that is very widely used in practice---it routinely features as a leading algorithm in machine learning competitions such as Kaggle and the…

Machine Learning · Computer Science 2020-09-17 Haihao Lu , Rahul Mazumder

The dual tasks of quantum Hamiltonian learning and quantum Gibbs sampling are relevant to many important problems in physics and chemistry. In the low temperature regime, algorithms for these tasks often suffer from intractabilities, for…

We present an efficient classical algorithm for training deep Boltzmann machines (DBMs) that uses rejection sampling in concert with variational approximations to estimate the gradients of the training objective function. Our algorithm is…

Machine Learning · Computer Science 2015-07-10 Nathan Wiebe , Ashish Kapoor , Christopher Granade , Krysta M Svore

Gaussian processes~(Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization…

Optimization and Control · Mathematics 2024-01-17 Artur M. Schweidtmann , Dominik Bongartz , Daniel Grothe , Tim Kerkenhoff , Xiaopeng Lin , Jaromil Najman , Alexander Mitsos

The 3D Gaussian splatting methods are getting popular. However, they work directly on the signal, leading to a dense representation of the signal. Even with some techniques such as pruning or distillation, the results are still dense. In…

Computer Vision and Pattern Recognition · Computer Science 2024-05-10 Yuanhao Gong