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cs.LG2025
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…
cs.LG2025
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…