Machine Learning
Machine learning (ML) focuses on developing algorithms that allow computers to learn from data without explicit programming, aiming to improve prediction accuracy, automate tasks, and extract insights. Current research emphasizes areas like fairness in federated learning, efficient model training and deployment (including techniques to reduce communication overhead), and enhancing model interpretability and robustness against adversarial attacks. ML's impact spans diverse fields, from healthcare (e.g., disease prediction) and industrial quality control to astrophysics (e.g., galaxy classification) and cybersecurity, demonstrating its broad applicability and significant potential for scientific advancement and practical problem-solving.
Papers
LossLens: Diagnostics for Machine Learning through Loss Landscape Visual Analytics
Tiankai Xie, Jiaqing Chen, Yaoqing Yang, Caleb Geniesse, Ge Shi, Ajinkya Chaudhari, John Kevin Cava, Michael W. Mahoney, Talita Perciano, Gunther H. Weber, Ross Maciejewski
GPgym: A Remote Service Platform with Gaussian Process Regression for Online Learning
Xiaobing Dai, Zewen Yang
Dual Interpretation of Machine Learning Forecasts
Philippe Goulet Coulombe, Maximilian Goebel, Karin Klieber
Development of an End-to-end Machine Learning System with Application to In-app Purchases
Dionysios Varelas, Elena Bonan, Lewis Anderson, Anders Englesson, Christoffer Åhrling, Adrian Chmielewski-Anders
Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression
Ruizhi Pu, Gezheng Xu, Ruiyi Fang, Binkun Bao, Charles X. Ling, Boyu Wang
Application of machine learning in grain-related clustering of Laue spots in a polycrystalline energy dispersive Laue pattern
Amir Tosson, Mohammad Shokr, Mahmoud Al Humaidi, Eduard Mikayelyan, Christian Gutt, Ulrich Pietsch
Probability-Informed Machine Learning
Mohsen Rashki
Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate
Fengpei Li, Haoxian Chen, Jiahe Lin, Arkin Gupta, Xiaowei Tan, Gang Xu, Yuriy Nevmyvaka, Agostino Capponi, Henry Lam
Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction
Andrea Cristina McGlinchey, Peter J Barclay
Early Detection of At-Risk Students Using Machine Learning
Azucena L. Jimenez Martinez, Kanika Sood, Rakeshkumar Mahto
An Algorithm-Centered Approach To Model Streaming Data
Fabian Hinder, Valerie Vaquet, David Komnick, Barbara Hammer
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets
Daniel Geissler, Dominique Nshimyimana, Vitor Fortes Rey, Sungho Suh, Bo Zhou, Paul Lukowicz
Pulling the Carpet Below the Learner's Feet: Genetic Algorithm To Learn Ensemble Machine Learning Model During Concept Drift
Teddy Lazebnik
Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data
Yihe Zhang, Bryce Turney, Purushottam Sigdel, Xu Yuan, Eric Rappin, Adrian Lago, Sytske Kimball, Li Chen, Paul Darby, Lu Peng, Sercan Aygun, Yazhou Tu, M. Hassan Najafi, Nian-Feng Tzeng
A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning
Akshaya Jagannadharao, Nicole Beckage, Sovan Biswas, Hilary Egan, Jamil Gafur, Thijs Metsch, Dawn Nafus, Giuseppe Raffa, Charles Tripp
Discover Physical Concepts and Equations with Machine Learning
Bao-Bing Li, Yi Gu, Shao-Feng Wu
Backdoor attacks on DNN and GBDT -- A Case Study from the insurance domain
Robin Kühlem (1), Daniel Otten (1), Daniel Ludwig (1), Anselm Hudde (1 and 3), Alexander Rosenbaum (2), Andreas Mauthe (2) ((1) Debeka, Koblenz, Germany, (2) Computer Science, University of Koblenz, Koblenz, Germany, (3) Department of Maths and Technology, Koblenz University of Applied Sciences, Remagen, Germany)
Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software
Zhenpeng Chen, Xinyue Li, Jie M. Zhang, Federica Sarro, Yang Liu