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
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning
Caleb J.S. Barr, Olivia Erdelyi, Paul D. Docherty, Randolph C. Grace
Gen-AI for User Safety: A Survey
Akshar Prabhu Desai, Tejasvi Ravi, Mohammad Luqman, Nithya Kota, Pranjul Yadav
MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning
Jiaxin Chen, Jinliang Ding, Kay Chen Tan, Jiancheng Qian, Ke Li
Prompts Matter: Comparing ML/GAI Approaches for Generating Inductive Qualitative Coding Results
John Chen, Alexandros Lotsos, Lexie Zhao, Grace Wang, Uri Wilensky, Bruce Sherin, Michael Horn
Impact of Fake News on Social Media Towards Public Users of Different Age Groups
Kahlil bin Abdul Hakim, Sathishkumar Veerappampalayam Easwaramoorthy
Sdn Intrusion Detection Using Machine Learning Method
Muhammad Zawad Mahmud, Shahran Rahman Alve, Samiha Islam, Mohammad Monirujjaman Khan
ICE-T: A Multi-Faceted Concept for Teaching Machine Learning
Hendrik Krone, Pierre Haritz, Thomas Liebig
Machine learning for prediction of dose-volume histograms of organs-at-risk in prostate cancer from simple structure volume parameters
Saheli Saha, Debasmita Banerjee, Rishi Ram, Gowtham Reddy, Debashree Guha, Arnab Sarkar, Bapi Dutta, Moses ArunSingh S, Suman Chakraborty, Indranil Mallick
Enhancing Depth Image Estimation for Underwater Robots by Combining Image Processing and Machine Learning
Quang Truong Nguyen, Thanh Nguyen Canh, Xiem HoangVan
Machine learning and optimization-based approaches to duality in statistical physics
Andrea E. V. Ferrari, Prateek Gupta, Nabil Iqbal
The Multiple Dimensions of Spuriousness in Machine Learning
Samuel J. Bell, Skyler Wang
Evaluating the Economic Implications of Using Machine Learning in Clinical Psychiatry
Soaad Hossain, James Rasalingam, Arhum Waheed, Fatah Awil, Rachel Kandiah, Syed Ishtiaque Ahmed
Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning
Carlos Huertas
Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-Awareness
Abdelmajid Essofi, Ridwan Salahuddeen, Munachiso Nwadike, Elnura Zhalieva, Kun Zhang, Eric Xing, Willie Neiswanger, Qirong Ho
Oblivious Defense in ML Models: Backdoor Removal without Detection
Shafi Goldwasser, Jonathan Shafer, Neekon Vafa, Vinod Vaikuntanathan
Graph-Based Semi-Supervised Segregated Lipschitz Learning
Farid Bozorgnia, Yassine Belkheiri, Abderrahim Elmoataz
Evaluating Machine Learning Models against Clinical Protocols for Enhanced Interpretability and Continuity of Care
Christel Sirocchi, Muhammad Suffian, Federico Sabbatini, Alessandro Bogliolo, Sara Montagna
[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI
Maren Pielka, Tobias Schneider, Jan Terheyden, Rafet Sifa