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cs.LG2026
PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks
Srijith Nair, Atilla Eryilmaz, Jia Liu
In the literature, many continual learning (CL) algorithms have been proposed to address the issue of catastrophic forgetting in ML models (i.e., learning new tasks leads to the lo…
cs.LG2025
FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation
Srijith Nair, Michael Lin, Peizhong Ju +3
Collaborative training methods like Federated Learning (FL) and Split Learning (SL) enable distributed machine learning without sharing raw data. However, FL assumes clients can tr…
cs.LG2024
PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization
Mingjing Xu, Peizhong Ju, Jia Liu +1
Multi-objective optimization (MOO) lies at the core of many machine learning (ML) applications that involve multiple, potentially conflicting objectives (e.g., multi-task learning,…