Clustering is a fundamental primitive in unsupervised learning which gives rise to a rich
class of computationally-challenging inference tasks. In this work, we focus on the canonical …

We study the complexity of learning mixtures of separated Gaussians with common
unknown bounded covariance matrix. Specifically, we focus on learning Gaussian mixture …

We study the problem of community recovery and detection in multi-layer stochastic block
models, focusing on the critical network density threshold for consistent community structure …

We develop and analyze a principled approach to kernel ridge regression under covariate
shift. The goal is to learn a regression function with small mean squared error over a target …

Tensor-on-tensor regression: Riemannian optimization, over-parameterization, statistical-computational
gap and their interplay Page 1 The Annals of Statistics 2024, Vol. 52, No. 6, 2583–2612 …

In the supplement [46], we first provide the proof of Theorem 2.3 in Appendix A, followed by
the proofs of results of Section 3.4 in Appendix B. The proof of Theorem 3.3 is given in …

Recovering a planted vector $ v $ in an $ n $-dimensional random subspace of $\mathbb
{R}^ N $ is a generic task related to many problems in machine learning and statistics, such …

Non-Gaussian Component Analysis (NGCA) is the statistical task of finding a non-Gaussian
direction in a high-dimensional dataset. Specifically, given iid samples from a distribution …

Computational lower bounds for graphon estimation via low-degree polynomials Page 1 The
Annals of Statistics 2024, Vol. 52, No. 5, 2318–2348 https://doi.org/10.1214/24-AOS2437 © …

Statistical-computational trade-offs in tensor PCA and related problems via communication
complexity Page 1 The Annals of Statistics 2024, Vol. 52, No. 1, 131–156 https://doi.org/10.1214/23-AOS2331 …