5 papers
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…
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 -dimens…
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 , called the pretraining…
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…
High-probability minimax lower bounds
Tianyi Ma, Kabir A. Verchand, Richard J. Samworth
The minimax risk is often considered as a gold standard against which we can compare specific statistical procedures. Nevertheless, as has been observed recently in robust and heav…