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ORCIDMaksim Rakhuba
Person information
- affiliation: ETH Zurich, Seminar for Applied Mathematics, Switzerland
- affiliation: Skolkovo Institute of Science and Technology, Moscow, Russia
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2020 – today
- 2026
[j13]Alexander Molozhavenko, Maxim V. Rakhuba:
Optimization on the extended tensor-train manifold with shared factors. Comput. Appl. Math. 45(6): 221 (2026)- 2025
- [c13]Ekaterina Grishina, Mikhail Gorbunov, Maxim V. Rakhuba:
ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations. ACL (Findings) 2025: 26937-26949 - [c12]Viacheslav Yusupov, Maxim V. Rakhuba, Evgeny Frolov:
Knowledge Graph Completion with Mixed Geometry Tensor Factorization. AISTATS 2025: 4924-4932 - [c11]Viacheslav Yusupov
, Maxim V. Rakhuba, Evgeny Frolov:
Ultra Fast Warm Start Solution for Graph Recommendations. CIKM 2025: 5469-5473 - [c10]Viacheslav Yusupov, Maxim V. Rakhuba, Evgeny Frolov:
Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendations. RecSys 2025: 1217-1221 - [i24]Viacheslav Yusupov, Maxim V. Rakhuba, Evgeny Frolov:
Knowledge Graph Completion with Mixed Geometry Tensor Factorization. CoRR abs/2504.02589 (2025) - [i23]Ekaterina Grishina, Mikhail Gorbunov, Maxim V. Rakhuba:
ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations. CoRR abs/2506.02818 (2025) - [i22]Ekaterina Grishina, Matvey Smirnov, Maxim V. Rakhuba:
Accelerating Newton-Schulz Iteration for Orthogonalization via Chebyshev-type Polynomials. CoRR abs/2506.10935 (2025) - [i21]Alexey Naumov, Maxim V. Rakhuba, Denis Ryapolov, Sergey Samsonov:
On the Upper Bounds for the Matrix Spectral Norm. CoRR abs/2506.15660 (2025) - [i20]Nikolay Yudin, Alexander Gaponov, Sergei Kudriashov, Maxim V. Rakhuba:
Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers. CoRR abs/2507.07814 (2025) - [i19]Vladimir Bogachev, Vladimir Aletov, Alexander Molozhavenko, Denis Bobkov, Vera Soboleva, Aibek Alanov, Maxim V. Rakhuba:
RiemannLoRA: A Unified Riemannian Framework for Ambiguity-Free LoRA Optimization. CoRR abs/2507.12142 (2025) - [i18]Askar Tsyganov, Evgeny Frolov, Sergey Samsonov, Maxim V. Rakhuba:
Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation. CoRR abs/2508.04444 (2025) - [i17]Viacheslav Yusupov, Maxim V. Rakhuba, Evgeny Frolov:
Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendations. CoRR abs/2508.11978 (2025) - [i16]Alexander Molozhavenko, Maxim V. Rakhuba:
Optimization on the Extended Tensor-Train Manifold with Shared Factors. CoRR abs/2508.20928 (2025) - [i15]Viacheslav Yusupov, Maxim V. Rakhuba, Evgeny Frolov:
Ultra Fast Warm Start Solution for Graph Recommendations. CoRR abs/2509.01549 (2025) - [i14]Nikolay Yudin, Ekaterina Grishina, Andrey Veprikov, Alexandr Beznosikov, Maxim V. Rakhuba:
DyKAF: Dynamical Kronecker Approximation of the Fisher Information Matrix for Gradient Preconditioning. CoRR abs/2511.06477 (2025) - 2024
- [c9]Ivan Peshekhonov, Aleksey Arzhantsev, Maxim V. Rakhuba:
Training a Tucker Model With Shared Factors: a Riemannian Optimization Approach. AISTATS 2024: 3304-3312 - [c8]Nikita Puchkin, Maxim V. Rakhuba:
Dimension-free Structured Covariance Estimation. COLT 2024: 4276-4306 - [c7]Ekaterina Grishina, Mikhail Gorbunov, Maxim V. Rakhuba:
Tight and Efficient Upper Bound on Spectral Norm of Convolutional Layers. ECCV (89) 2024: 19-34 - [c6]Mikhail Gorbunov, Nikolay Yudin, Vera Soboleva, Aibek Alanov, Alexey Naumov, Maxim V. Rakhuba:
Group and Shuffle: Efficient Structured Orthogonal Parametrization. NeurIPS 2024 - [i13]Mikhail Gorbunov, Nikolay Yudin, Vera Soboleva, Aibek Alanov, Alexey Naumov, Maxim V. Rakhuba:
Group and Shuffle: Efficient Structured Orthogonal Parametrization. CoRR abs/2406.10019 (2024) - [i12]Ekaterina Grishina, Mikhail Gorbunov, Maxim V. Rakhuba:
Tight and Efficient Upper Bound on Spectral Norm of Convolutional Layers. CoRR abs/2409.11859 (2024) - 2023
- [j12]Ivan V. Oseledets, Maxim V. Rakhuba, André Uschmajew:
Local convergence of alternating low-rank optimization methods with overrelaxation. Numer. Linear Algebra Appl. 30(3) (2023) - [j11]Lev I. Vysotsky, Maxim V. Rakhuba:
Tensor rank bounds and explicit QTT representations for the inverses of circulant matrices. Numer. Linear Algebra Appl. 30(3) (2023) - 2022
- [j10]Carlo Marcati, Maksim Rakhuba, Christoph Schwab:
Tensor rank bounds for point singularities in ℝ3. Adv. Comput. Math. 48(3): 18 (2022) - [j9]Vladimir A. Kazeev, Ivan V. Oseledets, Maxim V. Rakhuba, Christoph Schwab:
Quantized Tensor FEM for Multiscale Problems: Diffusion Problems in Two and Three Dimensions. Multiscale Model. Simul. 20(3): 893-935 (2022) - [j8]Alexander Novikov, Maxim V. Rakhuba, Ivan V. Oseledets:
Automatic Differentiation for Riemannian Optimization on Low-Rank Matrix and Tensor-Train Manifolds. SIAM J. Sci. Comput. 44(2): 843- (2022) - [c5]Alexandra Senderovich, Ekaterina Bulatova, Anton Obukhov, Maxim V. Rakhuba:
Towards Practical Control of Singular Values of Convolutional Layers. NeurIPS 2022 - [i11]Lev I. Vysotsky, Maxim V. Rakhuba:
Tensor rank bounds and explicit QTT representations for the inverses of circulant matrices. CoRR abs/2205.04335 (2022) - [i10]Alexandra Senderovich, Ekaterina Bulatova, Anton Obukhov, Maxim V. Rakhuba:
Towards Practical Control of Singular Values of Convolutional Layers. CoRR abs/2211.13771 (2022) - 2021
- [j7]Maxim V. Rakhuba:
Robust Alternating Direction Implicit Solver in Quantized Tensor Formats for a Three-Dimensional Elliptic PDE. SIAM J. Sci. Comput. 43(2): A800-A827 (2021) - [c4]Anton Obukhov, Maxim V. Rakhuba, Alexander Liniger, Zhiwu Huang, Stamatios Georgoulis, Dengxin Dai, Luc Van Gool:
Spectral Tensor Train Parameterization of Deep Learning Layers. AISTATS 2021: 3547-3555 - [c3]Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll, Maxim V. Rakhuba, Andreas Krause, Konrad Schindler:
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation. ICCV 2021: 11406-11415 - [i9]Anton Obukhov, Maxim V. Rakhuba, Alexander Liniger, Zhiwu Huang, Stamatios Georgoulis, Dengxin Dai, Luc Van Gool:
Spectral Tensor Train Parameterization of Deep Learning Layers. CoRR abs/2103.04217 (2021) - [i8]Alexander Novikov, Maxim V. Rakhuba, Ivan V. Oseledets:
Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds. CoRR abs/2103.14974 (2021) - [i7]Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll, Maxim V. Rakhuba, Andreas Krause, Konrad Schindler:
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation. CoRR abs/2105.14250 (2021) - [i6]Ivan V. Oseledets, Maxim V. Rakhuba, André Uschmajew:
Local convergence of alternating low-rank optimization methods with overrelaxation. CoRR abs/2111.14758 (2021) - 2020
- [c2]Anton Obukhov, Maxim V. Rakhuba, Stamatios Georgoulis, Menelaos Kanakis, Dengxin Dai, Luc Van Gool:
T-Basis: a Compact Representation for Neural Networks. ICML 2020: 7392-7404 - [i5]Vladimir A. Kazeev, Ivan V. Oseledets, Maksim Rakhuba, Christoph Schwab:
Quantized tensor FEM for multiscale problems: diffusion problems in two and three dimensions. CoRR abs/2006.01455 (2020) - [i4]Anton Obukhov, Maxim V. Rakhuba, Stamatios Georgoulis, Menelaos Kanakis, Dengxin Dai, Luc Van Gool:
T-Basis: a Compact Representation for Neural Networks. CoRR abs/2007.06631 (2020) - [i3]Carlo Marcati, Maxim V. Rakhuba, Johan E. M. Ulander:
Low rank tensor approximation of singularly perturbed partial differential equations in one dimension. CoRR abs/2010.06919 (2020)
2010 – 2019
- 2019
- [j6]Maxim V. Rakhuba, Alexander Novikov, Ivan V. Oseledets:
Low-rank Riemannian eigensolver for high-dimensional Hamiltonians. J. Comput. Phys. 396: 718-737 (2019) - [i2]Carlo Marcati, Maxim V. Rakhuba, Christoph Schwab:
Tensor Rank bounds for Point Singularities in R3. CoRR abs/1912.07996 (2019) - 2018
- [j5]Ivan V. Oseledets, Maxim V. Rakhuba, André Uschmajew:
Alternating Least Squares as Moving Subspace Correction. SIAM J. Numer. Anal. 56(6): 3459-3479 (2018) - [j4]M. V. Rakhuba, Ivan V. Oseledets:
Jacobi-Davidson Method on Low-Rank Matrix Manifolds. SIAM J. Sci. Comput. 40(2) (2018) - 2017
- [j3]Vladimir A. Kazeev, Ivan V. Oseledets, Maksim Rakhuba, Christoph Schwab:
QTT-finite-element approximation for multiscale problems I: model problems in one dimension. Adv. Comput. Math. 43(2): 411-442 (2017) - [i1]Valentin Khrulkov, M. V. Rakhuba, Ivan V. Oseledets:
Vico-Greengard-Ferrando quadratures in the tensor solver for integral equations. CoRR abs/1704.01669 (2017) - 2016
- [j2]M. V. Rakhuba, Ivan V. Oseledets:
Grid-based electronic structure calculations: The tensor decomposition approach. J. Comput. Phys. 312: 19-30 (2016) - 2015
- [j1]M. V. Rakhuba, Ivan V. Oseledets:
Fast Multidimensional Convolution in Low-Rank Tensor Formats via Cross Approximation. SIAM J. Sci. Comput. 37(2) (2015) - [c1]Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan V. Oseledets, Victor S. Lempitsky:
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition. ICLR (Poster) 2015
Coauthor Index
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