Shapley values and related probabilistic values provide a flexible model-agnostic framework for attributing the behavior of a black-box system to data points, features, or other components, with a wide range of applications from model explanation and data valuation to credit assignment and compensation. Their practical use, however, is limited by a fundamental computational challenge: exact evaluation requires considering exponentially many coalitions, while even a single coalition evaluation may involve expensive model training or inference. In this talk, I will present a unified first-order perspective on Monte Carlo estimation of probabilistic values. Although existing estimators are motivated by seemingly different strategies, many share a common error structure that reveals how coalition sampling and surrogate modeling jointly determine statistical efficiency. This perspective leads to an Efficiency-Aware Surrogate-adjusted Estimator (EASE) that directly minimizes an explicit first-order approximation to the mean squared error. We demonstrate that EASE consistently improves estimation accuracy compared to existing estimators under the same utility-evaluation budget.

Transformer-based large language models are increasingly used for long-horizon tasks. Solving such tasks requires persistent memory rather than simply relying on an expensive and ever-growing context window. In this talk, I'll present our recent work on a sleep-like consolidation mechanism in which a model periodically converts recent context into persistent fast weights before clearing its key-value cache. During "sleep", the model performs N offline recurrent passes over the accumulated context and updates the fast weights in its state-space model blocks. We show that increasing sleep duration N for our models improves performance, with the largest gains on examples that require deeper reasoning.

We perform a mathematical and statistical analysis of the Wasserstein least squares problem, a regression method for vector-valued covariates and distribution-valued responses. Our proposal contrasts with other distributional regression methods by having a direct interpretation in terms of random variables, as a nonparametric analogue of the classic random-effects model. On the mathematical side, we use a strategy of Lavenant (2024) to show that Wasserstein least squares is the canonical extension of Euclidean least squares to the space of probability distributions from the perspective of convex analysis; this viewpoint gives rise to multimarginal and dual formulations of the Wasserstein least squares problem, extending a similar theory for Wasserstein barycenters. We perform a statistical analysis of the Wasserstein least squares problem under the template deformation model, showing, surprisingly, that estimation is possible at the n^{-1/2} rate. As a special case, we obtain improved rates of estimation for Wasserstein barycenters, which are an exponential improvement over those established by Ahidar-Coutrix, Le Gouic and Paris (2020). Finally, we propose a heuristic particle method for Wasserstein least squares and use it to conduct a novel analysis of large-scale demographic data from the RAND Health and Retirement Study.

I'll give a quick overview on time-series foundation models: the premise, different architectures & models, how well they seem to do in practice, etc. Then present some results of our analysis in the context of infectious disease forecasting.