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
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning
Salman Haidri
AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning
Hideaki Takahashi
Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review
Nijat Mehdiyev, Maxim Majlatow, Peter Fettke
TSPP: A Unified Benchmarking Tool for Time-series Forecasting
Jan Bączek, Dmytro Zhylko, Gilberto Titericz, Sajad Darabi, Jean-Francois Puget, Izzy Putterman, Dawid Majchrowski, Anmol Gupta, Kyle Kranen, Pawel Morkisz
Attack Tree Analysis for Adversarial Evasion Attacks
Yuki Yamaguchi, Toshiaki Aoki
Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Open Source Data from PHM Data Challenges: A Review
Hanqi Su, Jay Lee
Uncertainty Quantification in Machine Learning for Joint Speaker Diarization and Identification
Simon W. McKnight, Aidan O. T. Hogg, Vincent W. Neo, Patrick A. Naylor
FairCompass: Operationalising Fairness in Machine Learning
Jessica Liu, Huaming Chen, Jun Shen, Kim-Kwang Raymond Choo
Anticipated Network Surveillance -- An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics
Aviral Srivastava, Dhyan Thakkar, Dr. Sharda Valiveti, Dr. Pooja Shah, Dr. Gaurang Raval
A universal approximation theorem for nonlinear resistive networks
Benjamin Scellier, Siddhartha Mishra
Robustness, Efficiency, or Privacy: Pick Two in Machine Learning
Youssef Allouah, Rachid Guerraoui, John Stephan
An effective and efficient green federated learning method for one-layer neural networks
Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas, Elena Hernández-Pereira, Beatriz Pérez-Sánchez
SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
Flying By ML -- CNN Inversion of Affine Transforms
L. Van Warren
DMC4ML: Data Movement Complexity for Machine Learning
Chen Ding, Christopher Kanan, Dylan McKellips, Toranosuke Ozawa, Arian Shahmirza, Wesley Smith
Room Occupancy Prediction: Exploring the Power of Machine Learning and Temporal Insights
Siqi Mao, Yaping Yuan, Yinpu Li, Ziren Wang, Yuanxin Yao, Yixin Kang