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
On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach
Sulaiman Aburakhia, Abdallah Shami, George K. Karagiannidis
Machine Learning on Blockchain Data: A Systematic Mapping Study
Georgios Palaiokrassas, Sarah Bouraga, Leandros Tassiulas
Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting
Busra Asan, Abdullah Akgül, Alper Unal, Melih Kandemir, Gozde Unal
An Incremental MaxSAT-based Model to Learn Interpretable and Balanced Classification Rules
Antônio Carlos Souza Ferreira Júnior, Thiago Alves Rocha
Improve accessibility for Low Vision and Blind people using Machine Learning and Computer Vision
Jasur Shukurov
Logic-based Explanations for Linear Support Vector Classifiers with Reject Option
Francisco Mateus Rocha Filho, Thiago Alves Rocha, Reginaldo Pereira Fernandes Ribeiro, Ajalmar Rêgo da Rocha Neto
Knowledge-guided Machine Learning: Current Trends and Future Prospects
Anuj Karpatne, Xiaowei Jia, Vipin Kumar
On the Detection of Anomalous or Out-Of-Distribution Data in Vision Models Using Statistical Techniques
Laura O'Mahony, David JP O'Sullivan, Nikola S. Nikolov
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
Jonathan Fuhr, Philipp Berens, Dominik Papies
Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatology
Dimitrios P. Panagoulias, Evridiki Tsoureli-Nikita, Maria Virvou, George A. Tsihrintzis
Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development
Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn, Burak Turhan
The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data
Alice Baird, Rachel Manzelli, Panagiotis Tzirakis, Chris Gagne, Haoqi Li, Sadie Allen, Sander Dieleman, Brian Kulis, Shrikanth S. Narayanan, Alan Cowen
Multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers
Ignacy Stępka, Mateusz Lango, Jerzy Stefanowski
Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations
Paulami Banerjee, Mohan Padmanabha, Chaitanya Sanghavi, Isabel Michel, Simone Gramsch
Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning
Yulu Gong, Jiaxin Huang, Bo Liu, Jingyu Xu, Binbin Wu, Yifan Zhang
Workload Estimation for Unknown Tasks: A Survey of Machine Learning Under Distribution Shift
Josh Bhagat Smith, Julie A. Adams