Abstract: I will introduce In-Context Operator Networks (ICON), a framework in which a single neural network learns solution operators for differential equations directly from a few prompted input-output examples at inference time, without any weight updates. ICON acts as a few-shot learner across forward and inverse problems for ODEs, PDEs, and mean-field control. I will […]
In multi-armed trials, adaptive designs are a popular way to increase estimation efficiency or identify optimal treatments. Many recent papers have proposed adaptive methods for minimizing the error of unbiased estimators that consider each treatment arm in isolation. This approach may be inefficient, because it fails to borrow shared information across the treatment arms. We […]
A Belyĭ pair \bigl( S,\, z(w) \bigr) is a compact, connected Riemann surface S together with a rational function z \colon S \to \mathbb{P}^1(\mathbb{C}) which has at most three critical values […]
A wide variety of medical imaging methods exist for research and diagnostic procedures on the Central Nervous System (CNS), among them Magnetic Resonance Imaging (MRI) and Optical Imaging. Mathematical modeling […]
Political scientists forecast elections, not primarily to satisfy public interest, but to validate statistical models used for estimating quantities of scholarly interest. Although scholars have learned a great deal from […]
This paper examines how initial conditions shape the long-run impact of large temporary shocks on economic geography. Using WWII supply contracts in the United States, we show that temporary spending […]
Large language models (LLMs) can use examples provided in a prompt to make predictions for new queries without updating their parameters, a capability known as in-context learning. In this talk, […]
