5 papers
High-dimensional sparsity-adaptive multiple change-point detection
Hyeyoung Maeng, Tengyao Wang, Piotr Fryzlewicz
We introduce a method for detecting multiple change-points in the mean of a high-dimensional data sequence. Unlike existing top-down (i.e. divisive) algorithms, we adopt a bottom-u…
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…
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…
Robust mean change point testing in high-dimensional data with heavy tails
Mengchu Li, Yudong Chen, Tengyao Wang +1
We study mean change point testing problems for high-dimensional data, with exponentially- or polynomially-decaying tails. In each case, depending on the -norm of the mean…