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Concept Bottleneck Models (CBMs) aim to enhance interpretability by predicting human-understandable concepts as intermediates for decision-making. However, these models often face challenges in ensuring reliable concept representations,…

Computer Vision and Pattern Recognition · Computer Science 2025-02-04 Yuxuan Cai , Xiyu Wang , Satoshi Tsutsui , Winnie Pang , Bihan Wen

The Bradley-Terry model is a popular approach to describe probabilities of the possible outcomes when elements of a set are repeatedly compared with one another in pairs. It has found many applications including animal behaviour, chess…

Methodology · Statistics 2015-03-17 Francois Caron , Arnaud Doucet

In this paper, an issue of building the RRC model using probability distributions other than beta distribution is addressed. More precisely, in this paper, we propose to build the RRR model using the truncated normal distribution. Heuristic…

Machine Learning · Computer Science 2019-05-27 Pawel Trajdos , Marek Kurzynski

The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new…

Machine Learning · Computer Science 2018-06-01 Kamran Kowsari , Mojtaba Heidarysafa , Donald E. Brown , Kiana Jafari Meimandi , Laura E. Barnes

Practitioners of Bayesian statistics have long depended on Markov chain Monte Carlo (MCMC) to obtain samples from intractable posterior distributions. Unfortunately, MCMC algorithms are typically serial, and do not scale to the large…

Machine Learning · Statistics 2015-06-11 Maxim Rabinovich , Elaine Angelino , Michael I. Jordan

This paper addresses the problem of summarizing the posterior distributions that typically arise, in a Bayesian framework, when dealing with signal decomposition problems with unknown number of components. Such posterior distributions are…

Computation · Statistics 2011-11-29 Alireza Roodaki , Julien Bect , Gilles Fleury

Generating radiology reports automatically reduces the workload of radiologists and helps the diagnoses of specific diseases. Many existing methods take this task as modality transfer process. However, since the key information related to…

Computer Vision and Pattern Recognition · Computer Science 2024-04-02 Yitian Tao , Liyan Ma , Jing Yu , Han Zhang

The MC$^3$ (Madigan and York, 1995) and Gibbs (George and McCulloch, 1997) samplers are the most widely implemented algorithms for Bayesian Model Averaging (BMA) in linear regression models. These samplers draw a variable at random in each…

Computation · Statistics 2013-06-26 Demetris Lamnisos , Jim E. Griffin , Mark F. J. Steel

Randomized Response (RR) is a protocol designed to collect and analyze categorical data with local differential privacy guarantees. It has been used as a building block of mechanisms deployed by Big tech companies to collect app or web…

Cryptography and Security · Computer Science 2026-01-14 Carlos Antonio Pinzón , Ehab ElSalamouny , Lucas Massot , Alexis Miller , Héber Hwang Arcolezi , Catuscia Palamidessi

We consider the Bayesian estimation of the parameters of a finite mixture model from independent order statistics arising from imperfect ranked set sampling designs. As a cost-effective method, ranked set sampling enables us to incorporate…

Markov Chain Monte Carlo (MCMC) is a popular class of statistical methods for simulating autocorrelated draws from target distributions, including posterior distributions in Bayesian analysis. An important consideration in using simulated…

Methodology · Statistics 2017-06-16 Benjamin E. Deonovic , Brian J. Smith

The Dirichlet distribution, also known as multivariate beta, is the most used to analyse frequencies or proportions data. Maximum likelihood is widespread for estimation of Dirichlet's parameters. However, for small sample sizes, the…

Methodology · Statistics 2021-03-04 Vincenzo Gioia , Euloge Clovis Kenne Pagui

Estimation of the mean vector and covariance matrix is of central importance in the analysis of multivariate data. In the framework of generalized linear models, usually the variances are certain functions of the means with the normal…

Methodology · Statistics 2023-01-25 Anupam Kundu , Mohsen Pourahmadi

The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian nonparametric modeling, and is widely used in tasks such as density estimation, natural language processing, and time series modeling. Although MCMC inference…

Machine Learning · Statistics 2013-04-09 Dan Lovell , Jonathan Malmaud , Ryan P. Adams , Vikash K. Mansinghka

Computing the marginal likelihood or evidence is one of the core challenges in Bayesian analysis. While there are many established methods for estimating this quantity, they predominantly rely on using a large number of posterior samples…

Computation · Statistics 2021-02-26 Eric Chuu , Debdeep Pati , Anirban Bhattacharya

In this paper, we introduce the MLM (Multiple Languages and Modalities) dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic…

Machine Learning · Computer Science 2020-10-27 Jason Armitage , Endri Kacupaj , Golsa Tahmasebzadeh , Swati , Maria Maleshkova , Ralph Ewerth , Jens Lehmann

PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster…

Gaussian Graphical Models (GGMs) are widely used to infer conditional dependence structures in high-dimensional data. However, standard precision matrix estimators are highly sensitive to data contamination, such as extreme outliers and…

Applications · Statistics 2026-03-25 Canruo Shen , Xintong Ji , Qiong Li , Wenzhi Yang , Xiaoping Shi

Normalized random measures (NRMs) provide a broad class of discrete random measures that are often used as priors for Bayesian nonparametric models. Dirichlet process is a well-known example of NRMs. Most of posterior inference methods for…

Machine Learning · Statistics 2015-11-19 Juho Lee , Seungjin Choi

Prompt learning with immensely large Casual Language Models (CLMs) has been shown promising for attribute-controllable text generation (CTG). However, vanilla prompt tuning tends to imitate training corpus characteristics beyond the control…

Computation and Language · Computer Science 2022-10-19 Hanqing Zhang , Dawei Song
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