LeArning Abstract
Learning, in the context of these papers, encompasses a broad range of research focused on improving the efficiency, robustness, and adaptability of machine learning models across diverse applications. Current efforts concentrate on developing novel self-supervised learning techniques, particularly for structured data like tabular formats, and on leveraging low-rank adaptations for efficient fine-tuning of large language and other foundation models. These advancements are significant because they address key challenges in data efficiency, computational cost, and the generalization capabilities of machine learning systems, impacting fields ranging from personalized medicine to autonomous robotics.
Papers
A Tidal Current Speed Forecasting Model based on Multiple Periodicity Learning
Tengfei Cheng, Yunxuan Dong, Yangdi Huang
Learning from the past: predicting critical transitions with machine learning trained on surrogates of historical data
Zhiqin Ma, Chunhua Zeng, Yi-Cheng Zhang, Thomas M. Bury
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning
Yiwei Zhang, Rouzbeh Behnia, Attila A. Yavuz, Reza Ebrahimi, Elisa Bertino
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
Jake Fawkes, Nic Fishman, Mel Andrews, Zachary C. Lipton
A New Perspective to Boost Performance Fairness for Medical Federated Learning
Yunlu Yan, Lei Zhu, Yuexiang Li, Xinxing Xu, Rick Siow Mong Goh, Yong Liu, Salman Khan, Chun-Mei Feng
The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models
Subhankar Maity, Aniket Deroy
Bridging Text and Image for Artist Style Transfer via Contrastive Learning
Zhi-Song Liu, Li-Wen Wang, Jun Xiao, Vicky Kalogeiton
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning
Yarden As, Bhavya Sukhija, Lenart Treven, Carmelo Sferrazza, Stelian Coros, Andreas Krause
Towards a Domain-Specific Modelling Environment for Reinforcement Learning
Natalie Sinani, Sahil Salma, Paul Boutot, Sadaf Mustafiz
Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similarity
Divyansh Jain, Eric Yang
SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction
Yang Zhou, Hao Shao, Letian Wang, Steven L. Waslander, Hongsheng Li, Yu Liu
Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributions
Xinyu Liao, Aoyang Qin, Jacob Seidman, Junqi Wang, Wei Wang, Paris Perdikaris
Slow Convergence of Interacting Kalman Filters in Word-of-Mouth Social Learning
Vikram Krishnamurthy, Cristian Rojas