| 2025 | ACL | ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations. | Ekaterina Grishina, Mikhail Gorbunov, Maxim V. Rakhuba |
| 2025 | AISTATS | Knowledge Graph Completion with Mixed Geometry Tensor Factorization. | Viacheslav Yusupov, Maxim V. Rakhuba, Evgeny Frolov |
| 2025 | CIKM | Ultra Fast Warm Start Solution for Graph Recommendations. | Viacheslav Yusupov, Maxim V. Rakhuba, Evgeny Frolov |
| 2025 | RecSys | Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendations. | Viacheslav Yusupov, Maxim V. Rakhuba, Evgeny Frolov |
| 2024 | AISTATS | Training a Tucker Model With Shared Factors: a Riemannian Optimization Approach. | Ivan Peshekhonov, Aleksey Arzhantsev, Maxim V. Rakhuba |
| 2024 | COLT | Dimension-free Structured Covariance Estimation. | Nikita Puchkin, Maxim V. Rakhuba |
| 2024 | ECCV | Tight and Efficient Upper Bound on Spectral Norm of Convolutional Layers. | Ekaterina Grishina, Mikhail Gorbunov, Maxim V. Rakhuba |
| 2021 | AISTATS | Spectral Tensor Train Parameterization of Deep Learning Layers. | Anton Obukhov, Maxim V. Rakhuba, Alexander Liniger, Zhiwu Huang, Stamatios Georgoulis, Dengxin Dai, Luc Van Gool |
| 2021 | ICCV | Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation. | Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll, Maxim V. Rakhuba, Andreas Krause, Konrad Schindler |
| 2020 | ICML | T-Basis: a Compact Representation for Neural Networks. | Anton Obukhov, Maxim V. Rakhuba, Stamatios Georgoulis, Menelaos Kanakis, Dengxin Dai, Luc Van Gool |