3 citations · 5 across the 10 of their papers we have counts for
8 papers · 1 filter
Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
Jialu Wang, Heinrich Peters, Asad A. Butt +6
Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training method…
Iterative Foundation Model Fine-Tuning on Multiple Rewards
Pouya M. Ghari, Simone Sciabola, Ye Wang
Fine-tuning foundation models has emerged as a powerful approach for generating objects with specific desired properties. Reinforcement learning (RL) provides an effective framewor…
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning
Pouya M. Ghari, Yanning Shen
Federated learning is renowned for its efficacy in distributed model training, ensuring that users, called clients, retain data privacy by not disclosing their data to the central…
Budgeted Online Model Selection and Fine-Tuning via Federated Learning
Pouya M. Ghari, Yanning Shen
Online model selection involves selecting a model from a set of candidate models 'on the fly' to perform prediction on a stream of data. The choice of candidate models henceforth h…
Personalized Online Federated Learning with Multiple Kernels
Pouya M. Ghari, Yanning Shen
Multi-kernel learning (MKL) exhibits well-documented performance in online non-linear function approximation. Federated learning enables a group of learners (called clients) to tra…
Graph-Assisted Communication-Efficient Ensemble Federated Learning
Pouya M Ghari, Yanning Shen
Communication efficiency arises as a necessity in federated learning due to limited communication bandwidth. To this end, the present paper develops an algorithmic framework where…
Online Learning with Uncertain Feedback Graphs
Pouya M Ghari, Yanning Shen
Online learning with expert advice is widely used in various machine learning tasks. It considers the problem where a learner chooses one from a set of experts to take advice and m…
Graph-Aided Online Multi-Kernel Learning
Pouya M Ghari, Yanning Shen
Multi-kernel learning (MKL) has been widely used in function approximation tasks. The key problem of MKL is to combine kernels in a prescribed dictionary. Inclusion of irrelevant k…