A new approach for sparse Bayesian channel estimation in SCMA uplink systems. - Kirill Struminsky; Stanislav Kruglik; Dmitry P. Vetrov; Ivan V. Oseledets (WCSP, 2016)

Robust Variational Inference. - Michael Figurnov; Kirill Struminsky; Dmitry P. Vetrov (CoRR, 2016)

Quantifying Learning Guarantees for Convex but Inconsistent Surrogates. - Kirill Struminsky; Simon Lacoste-Julien; Anton Osokin (NeurIPS, 2018)

The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models. - Andrei Atanov; Arsenii Ashukha; Kirill Struminsky; Dmitry P. Vetrov; Max Welling (CoRR, 2018)

A Simple Method to Evaluate Support Size and Non-uniformity of a Decoder-Based Generative Model. - Kirill Struminsky; Dmitry P. Vetrov (AIST, 2019)

The Deep Weight Prior. - Andrei Atanov; Arsenii Ashukha; Kirill Struminsky; Dmitry P. Vetrov; Max Welling (ICLR (Poster), 2019)

Low-Variance Black-Box Gradient Estimates for the Plackett-Luce Distribution. - Artyom Gadetsky; Kirill Struminsky; Christopher Robinson; Novi Quadrianto; Dmitry P. Vetrov (AAAI, 2020)

Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces. - Kirill Struminsky; Artyom Gadetsky; Denis Rakitin; Danil Karpushkin; Dmitry P. Vetrov (NeurIPS, 2021)

Differentiable Rendering with Reparameterized Volume Sampling. - Nikita Morozov; Denis Rakitin; Oleg Desheulin; Dmitry P. Vetrov; Kirill Struminsky (AISTATS, 2024)

CasTex: Cascaded Text-to-Texture Synthesis via Explicit Texture Maps and Physically-Based Shading. - Mishan Aliev; Dmitry Baranchuk; Kirill Struminsky (CoRR, 2025)

MADrive: Memory-Augmented Driving Scene Modeling. - Polina Karpikova; Daniil Selikhanovych; Kirill Struminsky; Ruslan Musaev; Maria Golitsyna; Dmitry Baranchuk (CoRR, 2025)