8 papers
Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer
Haotian Lin, Matthew Reimherr
The principal objective of this work is twofold within nonparametric regression settings: (1) to establish the minimax optimal convergence rates for fixed-bandwidth Gaussian kernel…
Pure Differential Privacy for Functional Summaries with a Laplace-like Process
Haotian Lin, Matthew Reimherr
Many existing mechanisms for achieving differential privacy (DP) on infinite-dimensional functional summaries typically involve embedding these functional summaries into finite-dim…
Doubly-Robust Functional Average Treatment Effect Estimation
Lorenzo Testa, Tobia Boschi, Francesca Chiaromonte +2
Understanding causal relationships in the presence of complex, structured data remains a central challenge in modern statistics and science in general. While traditional causal inf…
Efficient Difference-in-Differences Estimation when Outcomes are Missing at Random
Lorenzo Testa, Edward H. Kennedy, Matthew Reimherr
The Difference-in-Differences (DiD) method is a fundamental tool for causal inference, yet its application is often complicated by missing data. Although recent work has developed…
Temporal Functional Factor Analysis of Brain Connectivity
Kyle Stanley, Nicole Lazar, Matthew Reimherr
Many analyses of functional magnetic resonance imaging (fMRI) examine functional connectivity (FC), or the statistical dependencies among distant brain regions. These analyses are…
On Hypothesis Transfer Learning of Functional Linear Models
Haotian Lin, Matthew Reimherr
We study the transfer learning (TL) for the functional linear regression (FLR) under the Reproducing Kernel Hilbert Space (RKHS) framework, observing that the TL techniques in exis…