10 citations · 10 across the 4 of their papers we have counts for
4 papers · 1 filter
Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation
Ziyad Sheebaelhamd, Luca Viano, Volkan Cevher +1
This work investigates multi-objective imitation learning: the problem of recovering policies that lie on the Pareto front given demonstrations from multiple Pareto-optimal experts…
Efficient Personalization of Generative Models via Optimal Experimental Design
Guy Schacht, Ziyad Sheebaelhamd, Riccardo De Santi +2
Preference learning from human feedback has the ability to align generative models with the needs of end-users. Human feedback is costly and time-consuming to obtain, which creates…
Quantization-Free Autoregressive Action Transformer
Ziyad Sheebaelhamd, Michael Tschannen, Michael Muehlebach +1
Current transformer-based imitation learning approaches introduce discrete action representations and train an autoregressive transformer decoder on the resulting latent code. Howe…
Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces
Ziyad Sheebaelhamd, Konstantinos Zisis, Athina Nisioti +3
Multi-agent control problems constitute an interesting area of application for deep reinforcement learning models with continuous action spaces. Such real-world applications, howev…