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
Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence
Rambod Mojgani, Daniel Waelchli, Yifei Guan, Petros Koumoutsakos, Pedram Hassanzadeh
Machine Learning for Health symposium 2023 -- Findings track
Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Mercy Nyamewaa Asiedu, Serina Chang, Thomas Hartvigsen, Harvineet Singh
Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak
Yumou Wei, Ryan F. Forelli, Chris Hansen, Jeffrey P. Levesque, Nhan Tran, Joshua C. Agar, Giuseppe Di Guglielmo, Michael E. Mauel, Gerald A. Navratil
Hierarchy Representation of Data in Machine Learnings
Han Yegang, Park Minjun, Byun Duwon, Park Inkyu
More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory
James B. Simon, Dhruva Karkada, Nikhil Ghosh, Mikhail Belkin
Predicting Failure of P2P Lending Platforms through Machine Learning: The Case in China
Jen-Yin Yeh, Hsin-Yu Chiu, Jhih-Huei Huang
Towards Interpretable Classification of Leukocytes based on Deep Learning
Stefan Röhrl, Johannes Groll, Manuel Lengl, Simon Schumann, Christian Klenk, Dominik Heim, Martin Knopp, Oliver Hayden, Klaus Diepold
Prototype of deployment of Federated Learning with IoT devices
Pablo García Santaclara, Ana Fernández Vilas, Rebeca P. Díaz Redondo
Machine Learning For An Explainable Cost Prediction of Medical Insurance
Ugochukwu Orji, Elochukwu Ukwandu
MedISure: Towards Assuring Machine Learning-based Medical Image Classifiers using Mixup Boundary Analysis
Adam Byfield, William Poulett, Ben Wallace, Anusha Jose, Shatakshi Tyagi, Smita Shembekar, Adnan Qayyum, Junaid Qadir, Muhammad Bilal
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks
Mehdi Seyfi, Amin Banitalebi-Dehkordi, Zirui Zhou, Yong Zhang
Data Acquisition: A New Frontier in Data-centric AI
Lingjiao Chen, Bilge Acun, Newsha Ardalani, Yifan Sun, Feiyang Kang, Hanrui Lyu, Yongchan Kwon, Ruoxi Jia, Carole-Jean Wu, Matei Zaharia, James Zou
Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications
Tom Beucler, Erwan Koch, Sven Kotlarski, David Leutwyler, Adrien Michel, Jonathan Koh
Differentiable Visual Computing for Inverse Problems and Machine Learning
Andrew Spielberg, Fangcheng Zhong, Konstantinos Rematas, Krishna Murthy Jatavallabhula, Cengiz Oztireli, Tzu-Mao Li, Derek Nowrouzezahrai
Continuous Management of Machine Learning-Based Application Behavior
Marco Anisetti, Claudio A. Ardagna, Nicola Bena, Ernesto Damiani, Paolo G. Panero