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
Lexidate: Model Evaluation and Selection with Lexicase
Jose Guadalupe Hernandez, Anil Kumar Saini, Jason H. Moore
To Clip or not to Clip: the Dynamics of SGD with Gradient Clipping in High-Dimensions
Noah Marshall, Ke Liang Xiao, Atish Agarwala, Elliot Paquette
Understanding "Democratization" in NLP and ML Research
Arjun Subramonian, Vagrant Gautam, Dietrich Klakow, Zeerak Talat
Development of an Adaptive Multi-Domain Artificial Intelligence System Built using Machine Learning and Expert Systems Technologies
Jeremy Straub
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning
Michał Dereziński, Michael W. Mahoney
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
A tutorial on fairness in machine learning in healthcare
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