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Pull Requests (PRs) are central to collaborative coding, summarizing code changes for reviewers. However, many PR descriptions are incomplete, uninformative, or have out-of-context content, compromising developer workflows and hindering…

Software Engineering · Computer Science 2025-05-05 Kutay Tire , Berk Çakar , Eray Tüzün

As scientific data repositories and filesystems grow in size and complexity, they become increasingly disorganized. The coupling of massive quantities of data with poor organization makes it challenging for scientists to locate and utilize…

Information Retrieval · Computer Science 2018-10-16 Luann Jung , Brendan Whitaker , Kyle Chard , Aaron Elmore

Generative Artificial Intelligence (AI), such as large language models (LLMs), has become a transformative force across science, industry, and society. As these systems grow in popularity, web data becomes increasingly interwoven with this…

Machine Learning · Computer Science 2026-02-19 Kevin Wang , Hongqian Niu , Didong Li

Mislabeled, duplicated, or biased data in real-world scenarios can lead to prolonged training and even hinder model convergence. Traditional solutions prioritizing easy or hard samples lack the flexibility to handle such a variety…

Machine Learning · Computer Science 2023-11-08 Zhijie Deng , Peng Cui , Jun Zhu

Constructing prediction sets with coverage guarantees for unobserved outcomes is a core problem in modern statistics. Methods for predictive inference have been developed for a wide range of settings, but usually only consider test data…

Methodology · Statistics 2025-07-11 Yonghoon Lee , Eric Tchetgen Tchetgen , Edgar Dobriban

Given a {features, target} dataset, we introduce an incremental algorithm that constructs an aggregate regressor, using an ensemble of neural networks. It is well known that ensemble methods suffer from the multicollinearity issue, which is…

Machine Learning · Computer Science 2021-05-03 Pola Lydia Lagari , Lefteri H. Tsoukalas , Salar Safarkhani , Isaac E. Lagaris

Data imputation, the process of filling in missing feature elements for incomplete data sets, plays a crucial role in data-driven learning. A fundamental belief is that data imputation is helpful for learning performance, and it follows…

Machine Learning · Computer Science 2025-09-30 Ruikai Yang , Fan He , Mingzhen He , Kaijie Wang , Xiaolin Huang

Most modern neural networks for classification fail to take into account the concept of the unknown. Trained neural networks are usually tested in an unrealistic scenario with only examples from a closed set of known classes. In an attempt…

Machine Learning · Computer Science 2022-12-27 Justin Leo , Jugal Kalita

Automated program repair is an emerging technology that seeks to automatically rectify bugs and vulnerabilities using learning, search, and semantic analysis. Trust in automatically generated patches is necessary for achieving greater…

Software Engineering · Computer Science 2022-02-14 Yannic Noller , Ridwan Shariffdeen , Xiang Gao , Abhik Roychoudhury

Given a set of observations, feature acquisition is about finding the subset of unobserved features which would enhance accuracy. Such problems have been explored in a sequential setting in prior work. Here, the model receives feedback from…

Machine Learning · Computer Science 2023-12-21 Vedang Asgaonkar , Aditya Jain , Abir De

The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models. However, datasets often contain noisy data inadvertently included during the construction process. Numerous attempts have been…

Computation and Language · Computer Science 2024-09-25 Juhwan Choi , Jungmin Yun , Kyohoon Jin , YoungBin Kim

Missing data are an unavoidable complication in many machine learning tasks. When data are `missing at random' there exist a range of tools and techniques to deal with the issue. However, as machine learning studies become more ambitious,…

Data plays a central role in advancements in modern artificial intelligence, with high-quality data emerging as a key driver of model performance. This has prompted the development of principled and effective data curation methods in recent…

Machine Learning · Computer Science 2025-05-30 Shuran Zheng , Xuan Qi , Rui Ray Chen , Yongchan Kwon , James Zou

One of the major limitations of deep learning models is that they face catastrophic forgetting in an incremental learning scenario. There have been several approaches proposed to tackle the problem of incremental learning. Most of these…

Machine Learning · Computer Science 2021-02-04 Vinod K Kurmi , Badri N. Patro , Venkatesh K. Subramanian , Vinay P. Namboodiri

Numerous deep learning algorithms have been inspired by and understood via the notion of information bottleneck, where unnecessary information is (often implicitly) minimized while task-relevant information is maximized. However, a rigorous…

Machine Learning · Computer Science 2023-05-31 Kenji Kawaguchi , Zhun Deng , Xu Ji , Jiaoyang Huang

We study the problem of determining what data is required to solve a decision-making task when only partial information about the state of the world is available. Focusing on linear programs, we introduce a decision-focused notion of data…

Optimization and Control · Mathematics 2026-02-18 Omar Bennouna , Amine Bennouna , Saurabh Amin , Asuman Ozdaglar

Understanding and analyzing big data is firmly recognized as a powerful and strategic priority. For deeper interpretation of and better intelligence with big data, it is important to transform raw data (unstructured, semi-structured and…

Information Retrieval · Computer Science 2016-12-13 Seyed-Mehdi-Reza Beheshti , Alireza Tabebordbar , Boualem Benatallah , Reza Nouri

Most of statistics and AI draw insights through modelling discord or variance between sources of information (i.e., inter-source uncertainty). Increasingly, however, research is focusing upon uncertainty arising at the level of individual…

Machine Learning · Computer Science 2023-03-01 Shaily Kabir , Christian Wagner , Zack Ellerby

In real-world clinical settings, traditional deep learning-based classification methods struggle with diagnosing newly introduced disease types because they require samples from all disease classes for offline training. Class incremental…

Machine Learning · Computer Science 2024-06-11 Sana Ayromlou , Teresa Tsang , Purang Abolmaesumi , Xiaoxiao Li

Large Language Models have become the de facto approach to sequence-to-sequence text generation tasks, but for specialized tasks/domains, a pretrained LLM lacks specific capabilities to produce accurate or well-formatted responses.…

Computation and Language · Computer Science 2024-03-20 Jiuhai Chen , Jonas Mueller