8 citations · 11 across the 6 of their papers we have counts for
7 papers
Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers
Michelle Ching, Ioana Popescu, Nico Smith +3
We study in-context learning for nonparametric regression with -Hölder smooth regression functions, for some . We prove that, with in-context examples and -dimension…
Upgrading survival models with CARE
William G. Underwood, Henry W. J. Reeve, Oliver Y. Feng +3
Clinical risk prediction models are regularly updated as new data, often with additional covariates, become available. We propose CARE (Convex Aggregation of relative Risk Estimato…
Sharp Anti-Concentration Inequalities for Extremum Statistics via Copulas
Matias D. Cattaneo, Ricardo P. Masini, William G. Underwood
We derive sharp upper and lower bounds for the pointwise concentration function of the maximum statistic of identically distributed real-valued random variables. Our first main…
Inference with Mondrian Random Forests
Matias D. Cattaneo, Jason M. Klusowski, William G. Underwood
Random forests are popular methods for regression and classification analysis, and many different variants have been proposed in recent years. One interesting example is the Mondri…
Yurinskii's Coupling for Martingales
Matias D. Cattaneo, Ricardo P. Masini, William G. Underwood
Yurinskii's coupling is a popular theoretical tool for non-asymptotic distributional analysis in mathematical statistics and applied probability, offering a Gaussian strong approxi…
Uniform Inference for Kernel Density Estimators with Dyadic Data
Matias D. Cattaneo, Yingjie Feng, William G. Underwood
Dyadic data is often encountered when quantities of interest are associated with the edges of a network. As such it plays an important role in statistics, econometrics and many oth…
Motif-Based Spectral Clustering of Weighted Directed Networks
William George Underwood, Andrew Elliott, Mihai Cucuringu
Clustering is an essential technique for network analysis, with applications in a diverse range of fields. Although spectral clustering is a popular and effective method, it fails…