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Optimal Estimation of Discrete Multiview Distributions under Heteroskedastic Multinomial Sampling
Multiview latent-variable models provide a fundamental framework for discrete data analysis, with applications to latent structure models, topic models, and mixtures of product distributions. In the d ...
Preprint, 2026
Sharp Optimal Algorithm for Derivative-Free Stochastic Convex Optimization in One Dimension
Stochastic convex optimization is a classical problem with well-understood guarantees under first-order feedback. In contrast, for zero-order optimization with noisy function evaluations, a logarithmi ...
Arxiv, Optimization and Control, 2026
Gradient-free stochastic optimization for additive models
We address the problem of zero-order optimization from noisy observations for an objective function satisfying the Polyak-Łojasiewicz or the strong convexity condition. Additionally, we assume that t ...
Arxiv, Cornell University, 2025
Minimax estimation of functionals in sparse vector model with correlated observations
We consider the observations of an unknown s-sparse vector θ corrupted by Gaussian noise with zero mean and unknown covariance matrix Σ. We propose minimax optimal methods of estimating the ℓ2 n ...
Arxiv, Cornell University, 2024
Generalized multi-view model: Adaptive density estimation under low-rank constraints
We study the problem of bivariate discrete or continuous probability density estimation under low-rank constraints.For discrete distributions, we assume that the two-dimensional array to estimate is a ...
Arxiv, Cornell University, 2024
Benign overfitting and adaptive nonparametric regression
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arXiv [math.ST], 2022
Estimation of the l_2-norm and testing in sparse linear regression with unknown variance
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Bernoulli, 2022
Estimating the minimizer and the minimum value of a regression function under passive design
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arXiv [math.ST], 2022
Improved Clustering Algorithms for the Bipartite Stochastic Block Model
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IEEE Transactions on Information Theory, 2022