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Multiple Imputation (MI) is one of the most popular approaches to addressing missing values in questionnaires and surveys. MI with multivariate imputation by chained equations (MICE) allows flexible imputation of many types of data. In…

Methodology · Statistics 2023-04-24 Edoardo Costantini , Kyle M. Lang , Klaas Sijtsma , Tim Reeskens

The EM algorithm is a generic tool that offers maximum likelihood solutions when datasets are incomplete with data values missing at random or completely at random. At least for its simplest form, the algorithm can be rewritten in terms of…

Methodology · Statistics 2025-09-25 Daniel A. Griffith

Probabilistic inference algorithms for finding the most probable explanation, the maximum aposteriori hypothesis, and the maximum expected utility and for updating belief are reformulated as an elimination--type algorithm called bucket…

Artificial Intelligence · Computer Science 2013-02-18 Rina Dechter

This paper considers an extension of the multivariate symmetric Laplace distribution to matrix variate case. The symmetric Laplace distribution is a scale mixture of normal distribution. The maximum likelihood estimators (MLE) of the…

Statistics Theory · Mathematics 2025-09-18 Pooja Yadav , Tanuja Srivastava

Probabilistic databases play a crucial role in the management and understanding of uncertain data. However, incorporating probabilities into the semantics of incomplete databases has posed many challenges, forcing systems to sacrifice…

Databases · Computer Science 2015-03-17 Michael Wick , Andrew McCallum , Gerome Miklau

Large language models (LLMs) have shown remarkable capabilities in various natural language understanding tasks. With only a few demonstration examples, these LLMs can quickly adapt to target tasks without expensive gradient updates. Common…

Computation and Language · Computer Science 2023-11-14 Yue Yu , Jiaming Shen , Tianqi Liu , Zhen Qin , Jing Nathan Yan , Jialu Liu , Chao Zhang , Michael Bendersky

Decisions in organizations are about evaluating alternatives and choosing the one that would best serve organizational goals. To the extent that the evaluation of alternatives could be formulated as a predictive task with appropriate…

Human-Computer Interaction · Computer Science 2022-06-30 Charles Wan , Rodrigo Belo , Leid Zejnilović

The common way to optimize auction and pricing systems is to set aside a small fraction of the traffic to run experiments. This leads to the question: how can we learn the most with the smallest amount of data? For truthful auctions, this…

Computer Science and Game Theory · Computer Science 2021-11-09 Renato Paes Leme , Balasubramanian Sivan , Yifeng Teng , Pratik Worah

Triangular distributions are a well-known class of distributions that are often used as elementary example of a probability model. In the past, enumeration and order statistic-based methods have been suggested for the maximum likelihood…

Computation · Statistics 2016-02-16 Hien D Nguyen , Geoffrey J McLachlan

Selective inference aims at providing valid inference after a data-driven selection of models or hypotheses. It is essential to avoid overconfident results and replicability issues. While significant advances have been made in this area for…

Methodology · Statistics 2025-03-14 Matteo D'Alessandro , Magne Thoresen

A key challenge of modern machine learning systems is to achieve Out-of-Distribution (OOD) generalization -- generalizing to target data whose distribution differs from that of source data. Despite its significant importance, the…

Machine Learning · Statistics 2023-11-28 Jiawei Ge , Shange Tang , Jianqing Fan , Cong Ma , Chi Jin

Complex queries for massive data analysis jobs have become increasingly commonplace. Many such queries contain com- mon subexpressions, either within a single query or among multiple queries submitted as a batch. Conventional query…

Databases · Computer Science 2017-01-20 Tarun Kathuria , S. Sudarshan

With the increasing impact of algorithmic decision-making on human lives, the interpretability of models has become a critical issue in machine learning. Counterfactual explanation is an important method in the field of interpretable…

Machine Learning · Computer Science 2024-07-17 Ao Xu , Tieru Wu

Many representation schemes combining first-order logic and probability have been proposed in recent years. Progress in unifying logical and probabilistic inference has been slower. Existing methods are mainly variants of lifted variable…

Artificial Intelligence · Computer Science 2012-02-20 Vibhav Gogate , Pedro Domingos

Probabilistic mixture models have been widely used for different machine learning and pattern recognition tasks such as clustering, dimensionality reduction, and classification. In this paper, we focus on trying to solve the most common…

Machine Learning · Computer Science 2020-04-08 Gustavo A Valencia-Zapata , Daniel Mejia , Gerhard Klimeck , Michael Zentner , Okan Ersoy

Distributed learning of probabilistic models from multiple data repositories with minimum communication is increasingly important. We study a simple communication-efficient learning framework that first calculates the local maximum…

Machine Learning · Statistics 2014-10-13 Qiang Liu , Alexander Ihler

Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust models offer protection against mismatch between a model's…

Machine Learning · Statistics 2018-06-20 Yixin Wang , Alp Kucukelbir , David M. Blei

Anomaly estimation, or the problem of finding a subset of a dataset that differs from the rest of the dataset, is a classic problem in machine learning and data mining. In both theoretical work and in applications, the anomaly is assumed to…

Machine Learning · Computer Science 2021-06-14 Uthsav Chitra , Kimberly Ding , Jasper C. H. Lee , Benjamin J. Raphael

We propose an efficient algorithm for approximate computation of the profile maximum likelihood (PML), a variant of maximum likelihood maximizing the probability of observing a sufficient statistic rather than the empirical sample. The PML…

Machine Learning · Computer Science 2017-12-21 Dmitri S. Pavlichin , Jiantao Jiao , Tsachy Weissman

Estimations of physical parameters using data usually involve non-uniform experimental efficiencies. In this article, a method of maximum likelihood fit is introduced using the efficiency as a weight, while the probability distribution…

Data Analysis, Statistics and Probability · Physics 2023-08-31 Chenxu Yu , Yanxi Zhang