Learning curves provide insight into the dependence of a learner's generalization
performance on the training set size. This important tool can be used for model selection, to …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

The remarkable progress in deep learning in recent years is largely driven by improvements
in scale, where bigger models are trained on larger datasets for longer schedules. To predict …

When two different parties use the same learning rule on their own data, how can we test
whether the distributions of the two outcomes are similar? In this paper, we study the …

In this work, we aim to characterize the statistical complexity of realizable regression both in
the PAC learning setting and the online learning setting. Previous work had established the …

We present new upper and lower bounds on the number of learner mistakes in
thetransductive'online learning setting of Ben-David, Kushilevitz and Mansour (1997). This …

In this work we initiate the study of regression in the universal rates framework of Bousquet
et al. Unlike the traditional uniform learning setting, we are interested in obtaining learning …

Semi-supervised learning is the learning setting in which we have both labeled and
unlabeled data at our disposal. This survey covers theoretical results for this setting and …

Statistical learning theory is often associated with the principle of Occam's razor, which
recommends a simplicity preference in inductive inference. This paper distills the core …

When machine learning is outsourced to a rational agent, conflicts of interest might arise and
severely impact predictive performance. In this work, we propose a theoretical framework for …