Pointwise adaptation via stagewise aggregation of local estimates for multiclass classification. - Nikita Puchkin; Vladimir G. Spokoiny (CoRR, 2018)

Structure-adaptive Manifold Estimation. - Nikita Puchkin; Vladimir G. Spokoiny (J. Mach. Learn. Res., 2022)

Exponential Savings in Agnostic Active Learning Through Abstention. - Nikita Puchkin; Nikita Zhivotovskiy (IEEE Trans. Inf. Theory, 2022)

A Contrastive Approach to Online Change Point Detection. - Nikita Puchkin; Valeriia Shcherbakova (AISTATS, 2023)

Exploring Local Norms in Exp-concave Statistical Learning. - Nikita Puchkin; Nikita Zhivotovskiy (COLT, 2023)

Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations. - Denis Belomestny; Alexey Naumov; Nikita Puchkin; Sergey Samsonov (Neural Networks, 2023)

Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems. - Nikita Puchkin; Eduard Gorbunov; Nikolay Kutuzov; Alexander V. Gasnikov (AISTATS, 2024)

Dimension-free Structured Covariance Estimation. - Nikita Puchkin; Maxim V. Rakhuba (COLT, 2024)

Score-based change point detection via tracking the best of infinitely many experts. - Anna Markovich; Nikita Puchkin (CoRR, 2024)

Rates of convergence for density estimation with generative adversarial networks. - Nikita Puchkin; Sergey Samsonov; Denis Belomestny; Eric Moulines; Alexey Naumov (J. Mach. Learn. Res., 2024)

Generalization error bound for denoising score matching under relaxed manifold assumption. - Konstantin Yakovlev; Nikita Puchkin (COLT, 2025)

Dimension-free bounds in high-dimensional linear regression via error-in-operator approach. - Fedor Noskov; Nikita Puchkin; Vladimir G. Spokoiny (CoRR, 2025)

Sample complexity of Schrödinger potential estimation. - Nikita Puchkin; Iurii Pustovalov; Yuri Sapronov; Denis Suchkov; Alexey Naumov; Denis Belomestny (CoRR, 2025)