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
Unnatural Algorithms in Machine Learning
Christian Goodbrake
Perspectives on the State and Future of Deep Learning - 2023
Micah Goldblum, Anima Anandkumar, Richard Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson
Application of machine learning technique for a fast forecast of aggregation kinetics in space-inhomogeneous systems
M. A. Larchenko, R. R. Zagidullin, V. V. Palyulin, N. V. Brilliantov
SoK: Unintended Interactions among Machine Learning Defenses and Risks
Vasisht Duddu, Sebastian Szyller, N. Asokan
Estimating Fr\'echet bounds for validating programmatic weak supervision
Felipe Maia Polo, Mikhail Yurochkin, Moulinath Banerjee, Subha Maity, Yuekai Sun
Rapid detection of rare events from in situ X-ray diffraction data using machine learning
Weijian Zheng, Jun-Sang Park, Peter Kenesei, Ahsan Ali, Zhengchun Liu, Ian T. Foster, Nicholas Schwarz, Rajkumar Kettimuthu, Antonino Miceli, Hemant Sharma
On The Fairness Impacts of Hardware Selection in Machine Learning
Sree Harsha Nelaturu, Nishaanth Kanna Ravichandran, Cuong Tran, Sara Hooker, Ferdinando Fioretto
Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing
Lucas Monteiro Paes, Ananda Theertha Suresh, Alex Beutel, Flavio P. Calmon, Ahmad Beirami
Efficient Inverse Design Optimization through Multi-fidelity Simulations, Machine Learning, and Search Space Reduction Strategies
Luka Grbcic, Juliane Müller, Wibe Albert de Jong
Data-Centric Digital Agriculture: A Perspective
Ribana Roscher, Lukas Roth, Cyrill Stachniss, Achim Walter
A Cyclical Route Linking Fundamental Mechanism and AI Algorithm: An Example from Poisson's Ratio in Amorphous Networks
Changliang Zhu, Chenchao Fang, Zhipeng Jin, Baowen Li, Xiangying Shen, Lei Xu
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning
Omer Subasi, Oceane Bel, Joseph Manzano, Kevin Barker
All Rivers Run to the Sea: Private Learning with Asymmetric Flows
Yue Niu, Ramy E. Ali, Saurav Prakash, Salman Avestimehr
Lessons from Usable ML Deployments and Application to Wind Turbine Monitoring
Alexandra Zytek, Wei-En Wang, Sofia Koukoura, Kalyan Veeramachaneni
What Machine Learning Can Do for Focusing Aerogel Detectors
Foma Shipilov, Alexander Barnyakov, Vladimir Bobrovnikov, Sergey Kononov, Fedor Ratnikov
Reconsideration on evaluation of machine learning models in continuous monitoring using wearables
Cheng Ding, Zhicheng Guo, Cynthia Rudin, Ran Xiao, Fadi B Nahab, Xiao Hu
Intrusion Detection System with Machine Learning and Multiple Datasets
Haiyan Xuan, Mohith Manohar
A Comprehensive Literature Review on Sweet Orange Leaf Diseases
Yousuf Rayhan Emon, Md Golam Rabbani, Dr. Md. Taimur Ahad, Faruk Ahmed