I am broadly interested in minimax theory for hypothesis testing and functional estimation in nonparametric, high-dimensional, and robust statistics. More recently, I have been thinking about theoretical properties of score matching and score-based diffusion models, primarily from the statistical perspective.

Papers

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From optimal score matching to optimal sampling [arXiv]
Z. Dou, S. Kotekal, Z. Xu, and H. Zhou

Locally sharp goodness-of-fit testing in sup norm for high-dimensional counts [arXiv]
S. Kotekal, J. Chhor, and C. Gao

Variance estimation in compound decision theory under boundedness
S. Kotekal
Advances in Neural Information Processing Systems (NeurIPS) 2024, to appear

Optimal estimation of the null distribution in large-scale inference [arXiv]
S. Kotekal and C. Gao

Sparsity meets correlation in Gaussian sequence model [arXiv]
S. Kotekal and C. Gao

Optimal heteroskedasticity testing in nonparametric regression [arXiv]
S. Kotekal and S. Kundu
Annals of Statistics, to appear

Minimax signal detection in sparse additive models [arXiv]
S. Kotekal and C. Gao
IEEE Transactions on Information Theory, to appear

Minimax rates for sparse signal detection under correlation [doi]
S. Kotekal and C. Gao
Information and Inference, 2023

Statistical limits of sparse mixture detection [doi]
S. Kotekal
Electronic Journal of Statistics, 2022

Recurrent interactions can explain the variance in single trial responses [doi]
S. Kotekal and J. N. MacLean
PLOS Computational Biology, 2020