4 papers
An LP-based Sampling Policy for Multi-Armed Bandits with Side-Observations and Stochastic Availability
Ashutosh Soni, Peizhong Ju, Atilla Eryilmaz +1
We study the stochastic multi-armed bandit (MAB) problem where an underlying network structure enables side-observations across related actions. We use a bipartite graph to link ac…
Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual
Yining Li, Peizhong Ju, Ness Shroff
Reinforcement Learning from Human Feedback (RLHF) plays a significant role in aligning Large Language Models (LLMs) with human preferences. While RLHF with expected reward constrai…
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…
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,…