3 papers
cs.LG2025
Learning Confidence Bounds for Classification with Imbalanced Data
Matt Clifford, Jonathan Erskine, Alexander Hepburn +2
Class imbalance poses a significant challenge in classification tasks, where traditional approaches often lead to biased models and unreliable predictions. Undersampling and oversa…
cs.LG2025
Active Query Selection for Crowd-Based Reinforcement Learning
Jonathan Erskine, Taku Yamagata, Raúl Santos-RodrÃguez
Preference-based reinforcement learning has gained prominence as a strategy for training agents in environments where the reward signal is difficult to specify or misaligned with h…
cs.LG2025
Adaptive Pruning with Module Robustness Sensitivity: Balancing Compression and Robustness
Lincen Bai, Hedi Tabia, Raúl Santos-RodrÃguez
Neural network pruning has traditionally focused on weight-based criteria to achieve model compression, frequently overlooking the crucial balance between adversarial robustness an…