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cs.LG2024
Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift
Ozgu Goksu, Nicolas Pugeault
Federated Learning offers a solution for decentralised model training, addressing the difficulties associated with distributed data and privacy in machine learning. However, the fa…
cs.LG2024
Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach
Rory Young, Nicolas Pugeault
Deep reinforcement learning agents achieve state-of-the-art performance in a wide range of simulated control tasks. However, successful applications to real-world problems remain l…
cs.CV2024
The Bad Batches: Enhancing Self-Supervised Learning in Image Classification Through Representative Batch Curation
Ozgu Goksu, Nicolas Pugeault
The pursuit of learning robust representations without human supervision is a longstanding challenge. The recent advancements in self-supervised contrastive learning approaches hav…