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Multi-Task Bayesian In-Context Learning

Zhu 2026-06-18

Qingyang ZhuEric Karl OermannKyunghyun Cho

Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization. However, exact inference is often intractable, and scalable approximations may remain computationally expensive or require restrictive modeling assumptions that degrade predictive performance. Prior-Data Fitted and in-context models have recently emerged as an a

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Key Contributions

Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization.
However, exact inference is often intractable, and scalable approximations may remain computationally expensive or require restrictive modeling assumptions that degrade predictive performance.
Prior-Data Fitted and in-context models have recently emerged as an a

Research Themes

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