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
Bayesian Networks and Machine Learning for COVID-19 Severity Explanation and Demographic Symptom Classification
Oluwaseun T. Ajayi, Yu Cheng
ShareLoRA: Parameter Efficient and Robust Large Language Model Fine-tuning via Shared Low-Rank Adaptation
Yurun Song, Junchen Zhao, Ian G. Harris, Sangeetha Abdu Jyothi
Predicting Exoplanetary Features with a Residual Model for Uniform and Gaussian Distributions
Andrew Sweet
Improving the Validity and Practical Usefulness of AI/ML Evaluations Using an Estimands Framework
Olivier Binette, Jerome P. Reiter
Misam: Using ML in Dataflow Selection of Sparse-Sparse Matrix Multiplication
Sanjali Yadav, Bahar Asgari
Off-Policy Evaluation from Logged Human Feedback
Aniruddha Bhargava, Lalit Jain, Branislav Kveton, Ge Liu, Subhojyoti Mukherjee
Implementing engrams from a machine learning perspective: XOR as a basic motif
Jesus Marco de Lucas, Maria Peña Fernandez, Lara Lloret Iglesias
Explainable AI for Comparative Analysis of Intrusion Detection Models
Pap M. Corea, Yongxin Liu, Jian Wang, Shuteng Niu, Houbing Song
Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale
A. Feder Cooper
What is Fair? Defining Fairness in Machine Learning for Health
Jianhui Gao, Benson Chou, Zachary R. McCaw, Hilary Thurston, Paul Varghese, Chuan Hong, Jessica Gronsbell
Step-by-Step Diffusion: An Elementary Tutorial
Preetum Nakkiran, Arwen Bradley, Hattie Zhou, Madhu Advani
Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning
Christian Nauck, Anna Büttner, Sebastian Liemann, Frank Hellmann, Michael Lindner
An Effective Software Risk Prediction Management Analysis of Data Using Machine Learning and Data Mining Method
Jinxin Xu, Yue Wang, Ruisi Li, Ziyue Wang, Qian Zhao
Towards Integrating Personal Knowledge into Test-Time Predictions
Isaac Lage, Sonali Parbhoo, Finale Doshi-Velez
Using Quality Attribute Scenarios for ML Model Test Case Generation
Rachel Brower-Sinning, Grace A. Lewis, Sebastían Echeverría, Ipek Ozkaya
Machine Learning-Driven Open-Source Framework for Assessing QoE in Multimedia Networks
Parsa Hassani Shariat Panahi, Amir Hossein Jalilvand, Abolfazl Diyanat
Improving Noise Robustness through Abstractions and its Impact on Machine Learning
Alfredo Ibias, Karol Capala, Varun Ravi Varma, Anna Drozdz, Jose Sousa
A Sociotechnical Lens for Evaluating Computer Vision Models: A Case Study on Detecting and Reasoning about Gender and Emotion
Sha Luo, Sang Jung Kim, Zening Duan, Kaiping Chen
Malicious URL Detection using optimized Hist Gradient Boosting Classifier based on grid search method
Mohammad Maftoun, Nima Shadkam, Seyedeh Somayeh Salehi Komamardakhi, Zulkefli Mansor, Javad Hassannataj Joloudari