Branislav Kveton

Stabilizing advantage-weighted forward-process RL in flow models unlocks superior performance in image generation compared to reverse-process methods.

Forget reinforcement learning; this algorithm learns in real-time without any feedback at all.

Unlock face recognition with just one labeled example and a flood of unlabeled data, achieving state-of-the-art accuracy in a practical authentication scenario.

LLMs are revolutionizing conversational AI research, and this survey offers a structured guide to navigating the rapidly evolving landscape of LLM-powered user simulation.

Learning user preferences for thousands of items can be achieved with just a handful of evaluations, thanks to a novel approach that leverages effective dimension in graph-based bandit problems.

Correcting errors early in the diffusion process matters more than fixing them later: Stepwise-Flow-GRPO leverages this insight to dramatically improve RL-based flow model training.

Eye-tracking data can boost click prediction in carousel interfaces, but surprisingly, better click prediction doesn't always mean a better model of user behavior.

Stop wasting compute on LLM evals: a variance-adaptive querying strategy slashes estimation error by focusing on the most uncertain prompt-response pairs.