Fordetailedresults, please referto Table 2 and Table 3 in Appendix A.6.

Why Are Some Women Training for Pregnancy Like It's a Marathon? A growing legion of "zero trimester" influencers are convincing followers that healthy pregnancies are a choice--and that raw milk, watching sunsets, and pricey specialized courses can help. Three years ago, Esther Rohr and her husband decided to start thinking about pregnancy. The 26-year-old Oregon-based wedding photographer made small but intentional lifestyle changes--going to bed earlier, drinking more water and less alcohol, dialing in her fitness, loading up on protein, and taking supplements like beef organ capsules and Vitamin D3. They started charging their phones in the kitchen for better sleep and unplugging their Wi-Fi at night, because her research suggested it might affect cellular health. Concerned about their exposure to reproductive toxins, Rohr began the slow, painstaking task of swapping out all their synthetic workout clothes, nonstick pans, and scented personal care products that might contain phthalates or other endocrine-disrupting chemicals. She bought an air purifier and hopes to eventually replace their LED bulbs with incandescents, because she worries they might be affecting her circadian rhythm.

Texas A&M eliminates Women's and Gender Studies certificate program after reviewing 5,400 course syllabi, canceling six courses representing 0.11% of total offerings.

Adaptive sampling schemes are well known to create complex dependence that may invalidate conventional inference methods. A recent line of work shows that this need not be the case for UCB-type algorithms in multi-armed bandits. A central emerging theme is a `stability' property with asymptotically deterministic arm-pull counts in these algorithms, making inference as easy as in the i.i.d. setting. In this paper, we study the precise arm-pull dynamics in another canonical class of Thompson-sampling type algorithms. We show that the phenomenology is qualitatively different: the arm-pull count is asymptotically deterministic if and only if the arm is suboptimal or is the unique optimal arm; otherwise it converges in distribution to the unique invariant law of an SDE. This dichotomy uncovers a unifying principle behind many existing (in)stability results: an arm is stable if and only if its interaction with statistical noise is asymptotically negligible. As an application, we show that normalized arm means obey the same dichotomy, with Gaussian limits for stable arms and a semi-universal, non-Gaussian limit for unstable arms. This not only enables the construction of confidence intervals for the unknown mean rewards despite non-normality, but also reveals the potential of developing tractable inference procedures beyond the stable regime. The proofs rely on two new approaches. For suboptimal arms, we develop an `inverse process' approach that characterizes the inverse of the arm-pull count process via a Stieltjes integral. For optimal arms, we adopt a reparametrization of the arm-pull and noise processes that reduces the singularity in the natural SDE to proving the uniqueness of the invariant law of another SDE. We prove the latter by a set of analytic tools, including the parabolic Hörmander condition and the Stroock-Varadhan support theorem.

Deep neural networks (DNNs) achieve remarkable performance on a wide range of tasks, yet their mathematical analysis remains fragmented: stability and generalization are typically studied in disparate frameworks and on a case-by-case basis. Architecturally, DNNs rely on the recursive application of parametrized functions, a mechanism that can be unstable and difficult to train, making stability a primary concern. Even when training succeeds, there are few rigorous results on how well such models generalize beyond the observed data, especially in the generative setting. In this work, we leverage the theory of stochastic Iterated Function Systems (IFS) and show that two important deep architectures can be viewed as, or canonically associated with, place-dependent IFS. This connection allows us to import results from random dynamical systems to (i) establish the existence and uniqueness of invariant measures under suitable contractivity assumptions, and (ii) derive a Wasserstein generalization bound for generative modeling. The bound naturally leads to a new training objective that directly controls the collage-type approximation error between the data distribution and its image under the learned transfer operator. We illustrate the theory on a controlled 2D example and empirically evaluate the proposed objective on standard image datasets (MNIST, CelebA, CIFAR-10).

The government has launched a series of free AI training courses designed to help people learn how to use the technology at work. The online lessons give advice on things such as how to prompt chatbots or use them to assist with admin tasks. Many of the courses are free, with others subsidised, and the government aims to reach 10 million workers by 2030 - calling it the most ambitious training scheme since the launch of the Open University in 1971. But the Institute for Public Policy Research (IPPR) has warned workers will need to know more than just how to prompt a chatbot as the workforce adapts to the growth of AI. Skills for the age of AI can't be reduced to short technical courses alone, said Roa Powell, senior research fellow at the IPPR.

This article presents the full, original record of the 2024 Joint Statistical Meetings (JSM) town hall, "Statistics in the Age of AI," which convened leading statisticians to discuss how the field is evolving in response to advances in artificial intelligence, foundation models, large-scale empirical modeling, and data-intensive infrastructures. The town hall was structured around open panel discussion and extensive audience Q&A, with the aim of eliciting candid, experience-driven perspectives rather than formal presentations or prepared statements. This document preserves the extended exchanges among panelists and audience members, with minimal editorial intervention, and organizes the conversation around five recurring questions concerning disciplinary culture and practices, data curation and "data work," engagement with modern empirical modeling, training for large-scale AI applications, and partnerships with key AI stakeholders. By providing an archival record of this discussion, the preprint aims to support transparency, community reflection, and ongoing dialogue about the evolving role of statistics in the data- and AI-centric future.

Confirming which users are kids is politically fraught and a technical nightmare. Here's what moves from OpenAI and the FTC tell us. How do tech companies check if their users are kids? This question has taken on new urgency recently thanks to growing concern about the dangers that can arise when children talk to AI chatbots. For years Big Tech asked for birthdays (that one could make up) to avoid violating child privacy laws, but they weren't required to moderate content accordingly. Two developments over the last week show how quickly things are changing in the US and how this issue is becoming a new battleground, even among parents and child-safety advocates.