Maxim Rakhuba

Publications¹⁸

• Article Lev Vysotsky, Rakhuba M. Tensor rank bounds and explicit QTT representations for the inverses of circulant matrices // Numerical Linear Algebra with Applications. 2023. Vol. 30. No. 3. Article e2461. doi
• Article Novikov A., Rakhuba M., Oseledets I. Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds // SIAM Journal of Scientific Computing. 2022. Vol. 44. No. 2. P. A843-A869. doi
• Article Oseledets I. V., Maxim V. Rakhuba, Uschmajew A. Local convergence of alternating low-rank optimization methods with overrelaxation // Numerical Linear Algebra with Applications. 2022. P. 1-15. doi
• Article Marcati C., Rakhuba M., Ulander J. E. Low-rank tensor approximation of singularly perturbed boundary value problems in one dimension // Calcolo. 2022. Article 2. doi
• Article Kazeev V., Oseledets I., Maxim V. Rakhuba, Schwab C. Quantized Tensor FEM for Multiscale Problems: Diffusion Problems in Two and Three Dimensions // Multiscale Modeling and Simulation. 2022. Vol. 20. No. 3. P. 893-935. doi
• Article Marcati C., Rakhuba M., Schwab C. Tensor rank bounds for point singularities in ℝ³ // Advances in Computational Mathematics. 2022. Vol. 48. No. 3. Article 18. doi
• Chapter Senderovich A., Bulatova E., Obukhov A., Rakhuba M. Towards Practical Control of Singular Values of Convolutional Layers, in: Thirty-Sixth Conference on Neural Information Processing Systems : NeurIPS 2022. Curran Associates, Inc., 2022. P. 10918-10930.
• Chapter Usvyatsov M., Makarova A., Ballester-Ripoll R., Rakhuba M., Krause A., Schindler K. Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. , 2021. P. 11426-11435.
• Article Rakhuba M. Robust alternating direction implicit solver in quantized tensor formats for a three-dimensional elliptic PDE // SIAM Journal of Scientific Computing. 2021. Vol. 43. No. 2. P. A800-A827. doi
• Chapter Obukhov A., Rakhuba M., Liniger A., Huang Z., Georgoulis S., Dai D., Van Gool L. Spectral Tensor Train Parameterization of Deep Learning Layers, in: International Conference on Artificial Intelligence and Statistics, 13-15 April 2021, Virtual Vol. 130. PMLR, 2021. P. 3547-3555.
• Preprint Marcati C., Rakhuba M., Ulander J. Low rank tensor approximation of singularly perturbed partial differential equations in one dimension / Cornell University. Series math "arxiv.org". 2020. No. 2010.06919.
• Preprint Kazeev V., Oseledets I., Rakhuba M., Christoph S. Quantized tensor FEM for multiscale problems: diffusion problems in two and three dimensions / Cornell University. Series math "arxiv.org". 2020.
• Chapter Obukhov A., Rakhuba M., Kanakis M., Georgoulis S., Dai D., Van Gool L. T-Basis: a Compact Representation for Neural Networks, in: International Conference on Machine Learning (ICML 2020) Vol. 119. PMLR, 2020. P. 7392-7404.
• Preprint Marcati C., Rakhuba M., Christoph S. Tensor Rank bounds for Point Singularities in ℝ^3 / Cornell University. Series math "arxiv.org". 2020.
• Article Rakhuba Maxim, Novikov A., Oseledets I. Low-rank Riemannian eigensolver for high-dimensional Hamiltonians // Journal of Computational Physics. 2019. Vol. 396. P. 718-737. doi
• Preprint Rakhuba M. Robust solver in a quantized tensor format for three-dimensional elliptic problems / ETH Zurich. Series math "Seminar for Applied Mathematics reports". 2019. No. 30.
• Article Oseledets I., Rakhuba M., André U. Alternating Least Squares as Moving Subspace Correction // SIAM Journal on Numerical Analysis. 2018. Vol. 56. No. 6. P. 3459-3479. doi
• Article Rakhuba M., Oseledets I. Jacobi-Davidson Method on Low-Rank Matrix Manifolds // SIAM Journal of Scientific Computing. 2018. Vol. 40. No. 2. P. A1149-A1170. doi