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In multi-label classification, each training instance is associated with multiple class labels simultaneously. Unfortunately, collecting the fully precise class labels for each training instance is time- and labor-consuming for real-world…

Machine Learning · Computer Science 2024-03-26 Meng Wei , Zhongnian Li , Peng Ying , Yong Zhou , Xinzheng Xu

The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data…

Artificial Intelligence · Computer Science 2021-04-06 Ana Ozaki

Though the deep learning is pushing the machine learning to a new stage, basic theories of machine learning are still limited. The principle of learning, the role of the a prior knowledge, the role of neuron bias, and the basis for choosing…

Machine Learning · Statistics 2017-04-25 Hong Zhao

Reinforcement learning can acquire complex behaviors from high-level specifications. However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal…

Machine Learning · Computer Science 2016-05-30 Chelsea Finn , Sergey Levine , Pieter Abbeel

Many applications require the collection of data on different variables or measurements over many system performance metrics. We term those broadly as measures or variables. Often data collection along each measure incurs a cost, thus it is…

Methodology · Statistics 2021-11-30 Donghui Yan , Zhiwei Qin , Songxiang Gu , Haiping Xu , Ming Shao

The literature on machine teaching, machine education, and curriculum design for machines is in its infancy with sparse papers on the topic primarily focusing on data and model engineering factors to improve machine learning. In this paper,…

Artificial Intelligence · Computer Science 2020-02-11 Hussein A. Abbass , Sondoss Elsawah , Eleni Petraki , Robert Hunjet

We propose a reinforcement learning based approach to tackle the cost-sensitive learning problem where each input feature has a specific cost. The acquisition process is handled through a stochastic policy which allows features to be…

Machine Learning · Computer Science 2016-07-14 Gabriella Contardo , Ludovic Denoyer , Thierry Artières

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradation. To address these issues, machine unlearning has emerged…

Machine Learning · Computer Science 2024-04-08 Jie Xu , Zihan Wu , Cong Wang , Xiaohua Jia

Strategic classification studies learning settings in which individuals can modify their features, at a cost, in order to influence the classifier's decision. A central question is how the sample complexity of the induced (strategic)…

Machine Learning · Computer Science 2026-05-15 Yuval Filmus , Shay Moran , Elizaveta Nesterova , Nir Rosenfeld , Alexander Shlimovich

The definition is a common form of human expert knowledge, a building block of formal science and mathematics, a foundation for database theory and is supported in various forms in many knowledge representation and formal specification…

Logic in Computer Science · Computer Science 2017-02-16 Marc Denecker , Bart Bogaerts , Joost Vennekens

A growing body of research in continual learning focuses on the catastrophic forgetting problem. While many attempts have been made to alleviate this problem, the majority of the methods assume a single model in the continual learning…

Machine Learning · Computer Science 2023-07-06 Thang Doan , Seyed Iman Mirzadeh , Mehrdad Farajtabar

Self-supervised learning enables the training of large neural models without the need for large, labeled datasets. It has been generating breakthroughs in several fields, including computer vision, natural language processing, biology, and…

Computation and Language · Computer Science 2023-12-19 Luis Lugo , Valentin Vielzeuf

We introduce a dataset of concept learning tasks that helps uncover implicit biases in large language models. Using in-context concept learning experiments, we found that language models may have a bias toward upward monotonicity in…

Computation and Language · Computer Science 2025-11-27 Leroy Z. Wang

The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning. Deep learning methods are remarkably accurate, but also opaque, which limits their potential use in safety-critical applications. To…

This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an…

Statistical Finance · Quantitative Finance 2024-09-24 Kyungsub Lee

Learning to defer (L2D) aims to improve human-AI collaboration systems by learning how to defer decisions to humans when they are more likely to be correct than an ML classifier. Existing research in L2D overlooks key real-world aspects…

Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience. Humans can provide training data for machine learning applications and directly accomplish tasks that are hard for…

Machine Learning · Computer Science 2022-05-20 Xingjiao Wu , Luwei Xiao , Yixuan Sun , Junhang Zhang , Tianlong Ma , Liang He

Contemporary machine learning applications often involve classification tasks with many classes. Despite their extensive use, a precise understanding of the statistical properties and behavior of classification algorithms is still missing,…

Machine Learning · Computer Science 2020-11-17 Christos Thrampoulidis , Samet Oymak , Mahdi Soltanolkotabi

Real-world data is often incomplete and contains missing values. To train accurate models over real-world datasets, users need to spend a substantial amount of time and resources imputing and finding proper values for missing data items. In…

Machine Learning · Statistics 2024-03-05 Cheng Zhen , Nischal Aryal , Arash Termehchy , Alireza Aghasi , Amandeep Singh Chabada

Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this…

Computation and Language · Computer Science 2021-01-05 Shervin Minaee , Nal Kalchbrenner , Erik Cambria , Narjes Nikzad , Meysam Chenaghlu , Jianfeng Gao