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
Bridging Algorithmic Information Theory and Machine Learning: A New Approach to Kernel Learning
Boumediene Hamzi, Marcus Hutter, Houman Owhadi
Crowd management, crime detection, work monitoring using aiml
P. R. Adithya, Dheepak. S, B. Akash, Harshini. V, Sai Lakshana
Detecting subtle macroscopic changes in a finite temperature classical scalar field with machine learning
Jiming Yang, Yutong Zheng, Jiahong Zhou, Huiyu Li, Jun Yin
Adaptive Training Distributions with Scalable Online Bilevel Optimization
David Grangier, Pierre Ablin, Awni Hannun
Enhancing IoT Security via Automatic Network Traffic Analysis: The Transition from Machine Learning to Deep Learning
Mounia Hamidouche, Eugeny Popko, Bassem Ouni
Real-Time Surface-to-Air Missile Engagement Zone Prediction Using Simulation and Machine Learning
Joao P. A. Dantas, Diego Geraldo, Felipe L. L. Medeiros, Marcos R. O. A. Maximo, Takashi Yoneyama
Intelligent methods for business rule processing: State-of-the-art
Cristiano André da Costa, Uélison Jean Lopes dos Santos, Eduardo Souza dos Reis, Rodolfo Stoffel Antunes, Henrique Chaves Pacheco, Thaynã da Silva França, Rodrigo da Rosa Righi, Jorge Luis Victória Barbosa, Franklin Jebadoss, Jorge Montalvao, Rogerio Kunkel
Evolutionary Machine Learning and Games
Julian Togelius, Ahmed Khalifa, Sam Earle, Michael Cerny Green, Lisa Soros
Coarse-Grained Configurational Polymer Fingerprints for Property Prediction using Machine Learning
Ishan Kumar, Prateek K Jha
On the Relationship Between Interpretability and Explainability in Machine Learning
Benjamin Leblanc, Pascal Germain
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia
Astronomical Images Quality Assessment with Automated Machine Learning
Olivier Parisot, Pierrick Bruneau, Patrik Hitzelberger
Active Inference on the Edge: A Design Study
Boris Sedlak, Victor Casamayor Pujol, Praveen Kumar Donta, Schahram Dustdar
From Principle to Practice: Vertical Data Minimization for Machine Learning
Robin Staab, Nikola Jovanović, Mislav Balunović, Martin Vechev
Dates Fruit Disease Recognition using Machine Learning
Ghassen Ben Brahim, Jaafar Alghazo, Ghazanfar Latif, Khalid Alnujaidi
Surprisal Driven $k$-NN for Robust and Interpretable Nonparametric Learning
Amartya Banerjee, Christopher J. Hazard, Jacob Beel, Cade Mack, Jack Xia, Michael Resnick, Will Goddin
Adaptive Optimization Algorithms for Machine Learning
Slavomír Hanzely
A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data
Mahfuzur Rahman Chowdhury, Muhammad Ibrahim
Comparing Differentiable Logics for Learning Systems: A Research Preview
Thomas Flinkow, Barak A. Pearlmutter, Rosemary Monahan