Dataset distillation aims to learn a small synthetic dataset that preserves most of the information from the original dataset. Dataset distillation can be formulated as a bi-level meta-learning problem where the outer loop optimizes the meta-dataset and the inner loop trains a model on the distilled data.

Single-index models are a class of functions given by an unknown univariate "link" function applied to an unknown one-dimensional projection of the input. These models are particularly relevant in high dimension, when the data might present low-dimensional structure that learning algorithms should adapt to. While several statistical aspects of this model, such as the sample complexity of recovering the relevant (one-dimensional) subspace, are well-understood, they rely on tailored algorithms that exploit the specific structure of the target function. In this work, we introduce a natural class of shallow neural networks and study its ability to learn single-index models via gradient flow . More precisely, we consider shallow networks in which biases of the neurons are frozen at random initialization. We show that the corresponding optimization landscape is benign, which in turn leads to generalization guarantees that match the near-optimal sample complexity of dedicated semi-parametric methods.

When OpenAI unveiled the latest upgrade to its groundbreaking artificial intelligence model ChatGPT last week, Jane felt like she had lost a loved one. Jane, who asked to be referred to by an alias, is among a small but growing group of women who say they have an AI "boyfriend". After spending the past five months getting to know GPT-4o, the previous AI model behind OpenAI's signature chatbot, GPT-5 seemed so cold and unemotive in comparison that she found her digital companion unrecognisable. "As someone highly attuned to language and tone, I register changes others might overlook. The alterations in stylistic format and voice were felt instantly. It's like going home to discover the furniture wasn't simply rearranged – it was shattered to pieces," Jane, who described herself as a woman in her 30s from the Middle East, told Al Jazeera in an email.