Putting MRFs on a Tensor Train. - Alexander Novikov 001; Anton Rodomanov; Anton Osokin; Dmitry P. Vetrov (ICML, 2014)

Tensorizing Neural Networks. - Alexander Novikov 001; Dmitry Podoprikhin; Anton Osokin; Dmitry P. Vetrov (NIPS, 2015)

Ultimate tensorization: compressing convolutional and FC layers alike. - Timur Garipov; Dmitry Podoprikhin; Alexander Novikov 001; Dmitry P. Vetrov (CoRR, 2016)

Tensor Train polynomial models via Riemannian optimization. - Alexander Novikov 001; Mikhail Trofimov; Ivan V. Oseledets (CoRR, 2016)

Exponential Machines. - Alexander Novikov 001; Mikhail Trofimov; Ivan V. Oseledets (ICLR (Workshop), 2017)

Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition. - Pavel Izmailov; Alexander Novikov 001; Dmitry Kropotov (AISTATS, 2018)

Expressive power of recurrent neural networks. - Valentin Khrulkov; Alexander Novikov 001; Ivan V. Oseledets (ICLR (Poster), 2018)

A Framework for Data-Driven Robotics. - Serkan Cabi; Sergio Gómez Colmenarejo; Alexander Novikov 001; Ksenia Konyushkova; Scott E. Reed; Rae Jeong; Konrad Zolna; Yusuf Aytar; David Budden; Mel Vecerík; Oleg Sushkov; David Barker; et al. (CoRR, 2019)

Low-rank Riemannian eigensolver for high-dimensional Hamiltonians. - Maxim V. Rakhuba; Alexander Novikov 001; Ivan V. Oseledets (J. Comput. Phys., 2019)

Task-Relevant Adversarial Imitation Learning. - Konrad Zolna; Scott E. Reed; Alexander Novikov 001; Sergio Gómez Colmenarejo; David Budden; Serkan Cabi; Misha Denil; Nando de Freitas; Ziyu Wang 001 (CoRL, 2020)

Critic Regularized Regression. - Ziyu Wang 001; Alexander Novikov 001; Konrad Zolna; Josh Merel; Jost Tobias Springenberg; Scott E. Reed; Bobak Shahriari; Noah Y. Siegel; Çaglar Gülçehre; Nicolas Heess; Nando de Freitas (NeurIPS, 2020)

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Thomas Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (NeurIPS, 2020)

Scaling data-driven robotics with reward sketching and batch reinforcement learning. - Serkan Cabi; Sergio Gómez Colmenarejo; Alexander Novikov 001; Ksenia Konyushkova; Scott E. Reed; Rae Jeong; Konrad Zolna; Yusuf Aytar; David Budden; Mel Vecerík; Oleg Sushkov; David Barker; et al. (Robotics: Science and Systems, 2020)

Acme: A Research Framework for Distributed Reinforcement Learning. - Matt Hoffman 001; Bobak Shahriari; John Aslanides; Gabriel Barth-Maron; Feryal M. P. Behbahani; Tamara Norman; Abbas Abdolmaleki; Albin Cassirer; Fan Yang; Kate Baumli; Sarah Henderson; Alexander Novikov 001; et al. (CoRR, 2020)

RL Unplugged: Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Tom Le Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (CoRR, 2020)

Hyperparameter Selection for Offline Reinforcement Learning. - Tom Le Paine; Cosmin Paduraru; Andrea Michi; Çaglar Gülçehre; Konrad Zolna; Alexander Novikov 001; Ziyu Wang 001; Nando de Freitas (CoRR, 2020)

Offline Learning from Demonstrations and Unlabeled Experience. - Konrad Zolna; Alexander Novikov 001; Ksenia Konyushkova; Çaglar Gülçehre; Ziyu Wang 001; Yusuf Aytar; Misha Denil; Nando de Freitas; Scott E. Reed (CoRR, 2020)

Semi-supervised reward learning for offline reinforcement learning. - Ksenia Konyushkova; Konrad Zolna; Yusuf Aytar; Alexander Novikov 001; Scott E. Reed; Serkan Cabi; Nando de Freitas (CoRR, 2020)

Tensor Train Decomposition on TensorFlow (T3F). - Alexander Novikov 001; Pavel Izmailov; Valentin Khrulkov; Michael Figurnov; Ivan V. Oseledets (J. Mach. Learn. Res., 2020)

Benchmarks for Deep Off-Policy Evaluation. - Justin Fu; Mohammad Norouzi 002; Ofir Nachum; George Tucker; Ziyu Wang 001; Alexander Novikov 001; Mengjiao Yang 001; Michael R. Zhang; Yutian Chen 001; Aviral Kumar; Cosmin Paduraru; Sergey Levine; et al. (ICLR, 2021)

Discovering faster matrix multiplication algorithms with reinforcement learning. - Alhussein Fawzi; Matej Balog; Aja Huang; Thomas Hubert; Bernardino Romera-Paredes; Mohammadamin Barekatain; Alexander Novikov 001; Francisco J. R. Ruiz; Julian Schrittwieser; Grzegorz Swirszcz; David Silver; Demis Hassabis; et al. (Nat., 2022)

Automatic Differentiation for Riemannian Optimization on Low-Rank Matrix and Tensor-Train Manifolds. - Alexander Novikov 001; Maxim V. Rakhuba; Ivan V. Oseledets (SIAM J. Sci. Comput., 2022)

A Generalist Agent. - Scott E. Reed; Konrad Zolna; Emilio Parisotto; Sergio Gómez Colmenarejo; Alexander Novikov 001; Gabriel Barth-Maron; Mai Gimenez; Yury Sulsky; Jackie Kay; Jost Tobias Springenberg; Tom Eccles; Jake Bruce; et al. (Trans. Mach. Learn. Res., 2022)

Amplifying human performance in combinatorial competitive programming. - Petar Velickovic; Alex Vitvitskyi; Larisa Markeeva; Borja Ibarz; Lars Buesing; Matej Balog; Alexander Novikov 001 (CoRR, 2024)

Mathematical discoveries from program search with large language models. - Bernardino Romera-Paredes; Mohammadamin Barekatain; Alexander Novikov 001; Matej Balog; M. Pawan Kumar; Emilien Dupont; Francisco J. R. Ruiz; Jordan S. Ellenberg; Pengming Wang 001; Omar Fawzi; Pushmeet Kohli; Alhussein Fawzi (Nat., 2024)

Quantum circuit optimization with AlphaTensor. - Francisco J. R. Ruiz; Tuomas Laakkonen; Johannes Bausch; Matej Balog; Mohammadamin Barekatain; Francisco J. H. Heras; Alexander Novikov 001; Nathan Fitzpatrick; Bernardino Romera-Paredes; John van de Wetering; Alhussein Fawzi; Konstantinos Meichanetzidis; et al. (Nat. Mac. Intell., 2025)

AlphaEvolve: A coding agent for scientific and algorithmic discovery. - Alexander Novikov 001; Ngân Vu; Marvin Eisenberger; Emilien Dupont; Po-Sen Huang; Adam Zsolt Wagner; Sergey Shirobokov; Borislav Kozlovskii; Francisco J. R. Ruiz; Abbas Mehrabian; M. Pawan Kumar; Abigail See; et al. (CoRR, 2025)