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cs.LG2026
In-Context Learning for Pure Exploration
Alessio Russo, Ryan Welch, Aldo Pacchiano
We study the problem active sequential hypothesis testing, also known as pure exploration: given a new task, the learner adaptively collects data from the environment to efficientl…
cs.LG2024
Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
Ryan Welch, Jiaqi Zhang, Caroline Uhler
Causal disentanglement aims to learn about latent causal factors behind data, holding the promise to augment existing representation learning methods in terms of interpretability a…