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
Modeling Melt Pool Features and Spatter Using Symbolic Regression and Machine Learning
Olabode T. Ajenifujah, Amir Barati Farimani
RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova, Albert López-Brescó, Pere Barlet-Ros, Albert Cabellos-Aparicio
A Theory of Optimistically Universal Online Learnability for General Concept Classes
Steve Hanneke, Hongao Wang
Path Loss Prediction Using Machine Learning with Extended Features
Jonathan Ethier, Mathieu Chateauvert, Ryan G. Dempsey, Alexis Bose
Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning
Marios Aristodemou, Xiaolan Liu, Yuan Wang, Konstantinos G. Kyriakopoulos, Sangarapillai Lambotharan, Qingsong Wei
Early prediction of the transferability of bovine embryos from videomicroscopy
Yasmine Hachani (LACODAM), Patrick Bouthemy (SAIRPICO), Elisa Fromont (LACODAM), Sylvie Ruffini (UVSQ, INRAE), Ludivine Laffont (UVSQ, INRAE), Alline de Paula Reis (UVSQ, INRAE, ENVA)
deepTerra -- AI Land Classification Made Easy
Andrew Keith Wilkinson
Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML
Anees Al-Najjar, Nageswara S. V. Rao, Craig A. Bridges, Sheng Dai, Alex Walters
ML Mule: Mobile-Driven Context-Aware Collaborative Learning
Haoxiang Yu, Javier Berrocal, Christine Julien
Anonymization of Documents for Law Enforcement with Machine Learning
Manuel Eberhardinger, Patrick Takenaka, Daniel Grießhaber, Johannes Maucher
Bridging Smart Meter Gaps: A Benchmark of Statistical, Machine Learning and Time Series Foundation Models for Data Imputation
Amir Sartipi, Joaquin Delgado Fernandez, Sergio Potenciano Menci, Alessio Magitteri
Generating Poisoning Attacks against Ridge Regression Models with Categorical Features
Monse Guedes-Ayala, Lars Schewe, Zeynep Suvak, Miguel Anjos
A User's Guide to $\texttt{KSig}$: GPU-Accelerated Computation of the Signature Kernel
Csaba Tóth, Danilo Jr Dela Cruz, Harald Oberhauser
A group-theoretic framework for machine learning in hyperbolic spaces
Vladimir Jaćimović
Introduction to the Usage of Open Data from the Large Hadron Collider for Computer Scientists in the Context of Machine Learning
Timo Saala, Matthias Schott
Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
Ziming Wang, Changwu Huang, Ke Tang, Xin Yao
An Explainable Pipeline for Machine Learning with Functional Data
Katherine Goode, J. Derek Tucker, Daniel Ries, Heike Hofmann
Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning
Hankun He, Takuya Boehringer, Benjamin Schäfer, Kate Heppell, Christian Beck
Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization
Keita Kinjo