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
An Explainable AI Model for Predicting the Recurrence of Differentiated Thyroid Cancer
Mohammad Al-Sayed Ahmad, Jude Haddad
Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE's representing nonlinear dynamic systems
Ashish Pal, Sutanu Bhowmick, Satish Nagarajaiah
Enhancing Peer Review in Astronomy: A Machine Learning and Optimization Approach to Reviewer Assignments for ALMA
John M. Carpenter, Andrea Corvillón, Nihar B. Shah
How to unlearn a learned Machine Learning model ?
Seifeddine Achour
Learning from the past: predicting critical transitions with machine learning trained on surrogates of historical data
Zhiqin Ma, Chunhua Zeng, Yi-Cheng Zhang, Thomas M. Bury
Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques
Pohsun Feng, Ziqian Bi, Yizhu Wen, Benji Peng, Junyu Liu, Caitlyn Heqi Yin, Tianyang Wang, Keyu Chen, Sen Zhang, Ming Li, Jiawei Xu, Ming Liu, Xuanhe Pan, Jinlang Wang, Qian Niu
From Theory to Practice: Implementing and Evaluating e-Fold Cross-Validation
Christopher Mahlich, Tobias Vente, Joeran Beel
Data Deletion for Linear Regression with Noisy SGD
Zhangjie Xia, Chi-Hua Wang, Guang Cheng
An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data
Ryan King, Shivesh Kodali, Conrad Krueger, Tianbao Yang, Bobak J. Mortazavi
Time to Retrain? Detecting Concept Drifts in Machine Learning Systems
Tri Minh Triet Pham, Karthikeyan Premkumar, Mohamed Naili, Jinqiu Yang
Learning Algorithms Made Simple
Noorbakhsh Amiri Golilarz, Elias Hossain, Abdoljalil Addeh, Keyan Alexander Rahimi
Bank Loan Prediction Using Machine Learning Techniques
F M Ahosanul Haque, Md. Mahedi Hassan
A Systematic Survey on Large Language Models for Algorithm Design
Fei Liu, Yiming Yao, Ping Guo, Zhiyuan Yang, Zhe Zhao, Xi Lin, Xialiang Tong, Mingxuan Yuan, Zhichao Lu, Zhenkun Wang, Qingfu Zhang
SOAK: Same/Other/All K-fold cross-validation for estimating similarity of patterns in data subsets
Toby Dylan Hocking, Gabrielle Thibault, Cameron Scott Bodine, Paul Nelson Arellano, Alexander F Shenkin, Olivia Jasmine Lindly
MYCROFT: Towards Effective and Efficient External Data Augmentation
Zain Sarwar, Van Tran, Arjun Nitin Bhagoji, Nick Feamster, Ben Y. Zhao, Supriyo Chakraborty
Machine Learning for Missing Value Imputation
Abu Fuad Ahmad, Khaznah Alshammari, Istiaque Ahmed, MD Shohel Sayed
Impact of Missing Values in Machine Learning: A Comprehensive Analysis
Abu Fuad Ahmad, Md Shohel Sayeed, Khaznah Alshammari, Istiaque Ahmed
Active Fourier Auditor for Estimating Distributional Properties of ML Models
Ayoub Ajarra, Bishwamittra Ghosh, Debabrota Basu
Machine Learning-based feasibility estimation of digital blocks in BCD technology
Gabriele Faraone, Francesco Daghero, Eugenio Serianni, Dario Licastro, Nicola Di Carolo, Michelangelo Grosso, Giovanna Antonella Franchino, Daniele Jahier Pagliari
MEMS Gyroscope Multi-Feature Calibration Using Machine Learning Technique
Yaoyao Long, Zhenming Liu, Cong Hao, Farrokh Ayazi