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paradigms rely on a fragile "closed-world" assumption: that the unlabeled pool perfectly reflects the distribution of the labelled seed set. In real-world deployments, this is rarely true. Data streams
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discovery. Bayesian approaches provide a principled framework for modeling uncertainty by capturing posterior distributions over model parameters or predictions. Despite recent progress in approximate
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hospital or population often fail when applied elsewhere due to distributional shifts. Since acquiring new labeled data is often costly or infeasible due to rare diseases, limited expert availability, and
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, from swarm robotics to mesh networks. The prototypical model system for the investigation of self-organised task allocation are social insect colonies, such as bees and ants. They are able to distribute
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-Enhanced Learning Analytics for Adaptive Early Intervention in Higher Education Bayesian Uncertainty Estimation for Robust Single- and Multi-View Learning in CV and NLP Robust Active Learning Under
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that both parameter estimation and model selection can be interpreted as problems of data compression. The principle is simple: if we can compress data, we have learned something about its underlying
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. Wallace (1996). MML estimation of the parameters of the spherical Fisher Distribution. In S. Arikawa and A. K. Sharma (eds.), Proc. 7th International Workshop on Algorithmic Learning Theory (ALT'96
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representations complicate transparency and compliance checks with data protection and privacy legislation (e.g., GDPR) whether performed by humans or computer systems. Second, both privacy-preserving distributed
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Education Bayesian Uncertainty Estimation for Robust Single- and Multi-View Learning in CV and NLP Robust Active Learning Under Distribution Drift Data-Efficient Deep Learning for De Novo Molecular Design
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of Unknown Functions Quantum-Enhanced Learning Analytics for Adaptive Early Intervention in Higher Education Bayesian Uncertainty Estimation for Robust Single- and Multi-View Learning in CV and NLP Robust