Author

M. Alfarra

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#machine learning Preprint Oct 2026

Efficient Test-Time Adaptation For Robot Policies

Reinforcement-learning policies for robot locomotion can fail at deployment when operating conditions shift (e.g., rough or slippery terrain, payload changes, sensing degradation), and retraining or manual recalibration is often infeasible under on-device latency and memory constraints. We study test-time adaptation (T...

M. Alfarra, Pietro Mazzaglia, Markus Peschl et al. · 0 citations

Preprint Aug 2026

DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization

DiSCO is proposed, a zero-shot, strictly black-box defense that operates entirely at the prompt level as a plug-and-play module, requiring no model retraining, fine-tuning, or access to model internals, and can be readily applied to any text-to-image system without necessitating any changes to the model itself.

Tong Zhang, M. Alfarra, Carlos Hinojosa et al. · 0 citations