Scaling hyperparameter optimisation to very large datasets remains an open problem in the Gaussian process community.

The resulting features are evaluated on k-nearest neighbor classification over 11 datasets from vision, 5 from natural language processing, and 2 from audio.

Self-training often falls short under distribution shifts due to an increased discrepancy between prediction confidence and actual accuracy.

Frontier AI systems, including large language models (LLMs), hold increasing influence over the epistemology of human users. Such influence can reinforce prevailing societal values, potentially contributing to the lock-in of misguided moral beliefs and, consequently, the perpetuation of problematic moral practices on a broad scale. We introduce progress alignment as a technical solution to mitigate this imminent risk. Progress alignment algorithms learn to emulate the mechanics of human moral progress, thereby addressing the susceptibility of existing alignment methods to contemporary moral blindspots.

Bilevel optimization methods solve problems with hierarchical structures, optimizing two interdependent objectives: an inner-level objective and an outer-level one. Initially used in machine learning for model selection [Bennett et al., 2006] and sparse feature learning [Mairal et al., 2012], these methods gained popularity as efficient alternatives to grid search for hyper-parameter tuning [Feurer and Hutter,