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This paper proposes a general modeling framework that allows for uncertainty quantification at the individual covariate level and spatial referencing, operating withing a double generalized linear model (DGLM). DGLMs provide a general…

Methodology · Statistics 2023-02-14 Aritra Halder , Shariq Mohammed , Kun Chen , Dipak K. Dey

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

We analyze a new robust method for the reconstruction of probability distributions of observed data in the presence of output outliers. It is based on a so-called gradient conjugate prior (GCP) network which outputs the parameters of a…

Machine Learning · Statistics 2019-05-22 Pavel Gurevich , Hannes Stuke

Most machine learning models operate under the assumption that the training, testing and deployment data is independent and identically distributed (i.i.d.). This assumption doesn't generally hold true in a natural setting. Usually, the…

Machine Learning · Computer Science 2021-12-14 Kumud Lakara , Akshat Bhandari , Pratinav Seth , Ujjwal Verma

Transient stability-constrained preventive redispatch plays a crucial role in ensuring power system security and stability. Since redispatch strategies need to simultaneously satisfy complex transient constraints and the economic need,…

Systems and Control · Electrical Eng. & Systems 2024-07-02 Zhengcheng Wang , Fei Teng , Yanzhen Zhou , Qinglai Guo , Hongbin Sun

Composition-the ability to generate myriad variations from finite means-is believed to underlie powerful generalization. However, compositional generalization remains a key challenge for deep learning. A widely held assumption is that…

Machine Learning · Computer Science 2025-05-27 Qiyao Liang , Daoyuan Qian , Liu Ziyin , Ila Fiete

Due to their conjugate posteriors, Gaussian process priors are attractive for estimating the drift of stochastic differential equations with continuous time observations. However, their performance strongly depends on the choice of the…

Statistics Theory · Mathematics 2020-02-04 Jan van Waaij

Scale-mixture shrinkage priors have recently been shown to possess robust empirical performance and excellent theoretical properties such as model selection consistency and (near) minimax posterior contraction rates. In this paper, the…

Methodology · Statistics 2022-12-27 Ahmed Alhamzawi , Gorgees Shaheed Mohammad

RGB-to-hyperspectral image reconstruction is a highly ill-posed inverse problem, since multiple plausible spectral distributions may correspond to the same RGB observation. Existing regression-based methods usually learn a deterministic…

Computer Vision and Pattern Recognition · Computer Science 2026-05-08 Songyu Ding , Ronggiang Zhao , Mingchun Sun , Jie Liu

Probabilistic regression models the entire predictive distribution of a response variable, offering richer insights than classical point estimates and directly allowing for uncertainty quantification. While diffusion-based generative models…

Machine Learning · Computer Science 2025-10-07 Carlo Kneissl , Christopher Bülte , Philipp Scholl , Gitta Kutyniok

Global-local shrinkage hierarchies are an important innovation in Bayesian estimation. We propose the use of log-scale distributions as a novel basis for generating familes of prior distributions for local shrinkage hyperparameters. By…

Statistics Theory · Mathematics 2020-01-31 Daniel F. Schmidt , Enes Makalic

Domain generalization (DG) intends to train a model on multiple source domains to ensure that it can generalize well to an arbitrary unseen target domain. The acquisition of domain-invariant representations is pivotal for DG as they possess…

Computer Vision and Pattern Recognition · Computer Science 2024-01-12 Na Wang , Lei Qi , Jintao Guo , Yinghuan Shi , Yang Gao

The testing-based approach is a fundamental tool for establishing posterior contraction rates. Although the Hellinger metric is attractive owing to the existence of a desirable test function, it is not directly applicable in Gaussian…

Statistics Theory · Mathematics 2025-06-26 Seonghyun Jeong

Datasets with extreme observations and/or heavy-tailed error distributions are commonly encountered and should be analyzed with careful consideration of these features from a statistical perspective. Small deviations from an assumed model,…

Methodology · Statistics 2023-01-12 Meadhbh O'Neill , Kevin Burke

Although linear regression models are fundamental tools in statistical science, the estimation results can be sensitive to outliers. While several robust methods have been proposed in frequentist frameworks, statistical inference is not…

Methodology · Statistics 2020-07-15 Shintaro Hashimoto , Shonosuke Sugasawa

Dataset distillation (DD) has emerged as a widely adopted technique for crafting a synthetic dataset that captures the essential information of a training dataset, facilitating the training of accurate neural models. Its applications span…

Machine Learning · Computer Science 2025-02-04 Saeed Vahidian , Mingyu Wang , Jianyang Gu , Vyacheslav Kungurtsev , Wei Jiang , Yiran Chen

We introduce an architecture based on deep hierarchical decompositions to learn effective representations of large graphs. Our framework extends classic R-decompositions used in kernel methods, enabling nested part-of-part relations. Unlike…

Machine Learning · Computer Science 2024-03-19 Francesco Orsini , Daniele Baracchi , Paolo Frasconi

High-dimensional prediction considers data with more variables than samples. Generic research goals are to find the best predictor or to select variables. Results may be improved by exploiting prior information in the form of co-data,…

Methodology · Statistics 2022-05-17 Mirrelijn M. van Nee , Lodewyk F. A. Wessels , Mark A. van de Wiel

Recently, several studies have proven the global convergence and generalization abilities of the gradient descent method for two-layer ReLU networks. Most studies especially focused on the regression problems with the squared loss function,…

Machine Learning · Statistics 2020-03-19 Atsushi Nitanda , Geoffrey Chinot , Taiji Suzuki

We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building upon a global-local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are…

Methodology · Statistics 2019-07-02 Daniel R. Kowal , David S. Matteson , David Ruppert
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