R. Samworth

collaborators

12 papers

stat.ME2026

Deep learning with missing data

Tianyi Ma, Tengyao Wang, Richard J. Samworth

In the context of multivariate nonparametric regression with missing covariates, we propose Pattern Embedded Neural Networks (PENNs), which can be applied in conjunction with any e…

stat.ML2026

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 $I^\pm$ -HÃ¶lder smooth regression functions, for some $I^\pm > 0$ . We prove that, with $n$ in-context examples and $d$ -dimens…

stat.ML2026

Optimal In-context Adaptivity and Distributional Robustness of Transformers

Tianyi Ma, Tengyao Wang, Richard J. Samworth

We study in-context learning problems where a Transformer is pretrained on tasks drawn from a mixture distribution $I ¨ = \sum_{I^\pm \in A} I^»_{I^} \pm I ¨ _{I^} \pm$ , called the pretraining…

math.ST2026

Estimation beyond Missing (Completely) at Random

Tianyi Ma, Kabir A. Verchand, Thomas B. Berrett +2

We study the effects of missingness on the estimation of population parameters. Moving beyond restrictive missing completely at random (MCAR) assumptions, we first formulate a miss…

stat.ME2026

Testing conditional independence under isotonicity

Rohan Hore, Jake A. Soloff, Rina Foygel Barber +1

We propose a test of the conditional independence of random variables $X$ and~ $Y$ given~ $Z$ under the additional assumption that $X$ is stochastically nondecreasing in~ $Z$ . The wel…

stat.ME2026

Outrigger local polynomial regression

Elliot H. Young, Rajen D. Shah, Richard J. Samworth

Standard local polynomial estimators of a nonparametric regression function employ a weighted least squares loss function that is tailored to the setting of homoscedastic Gaussian…