Alexander Novikov

According to our database, Alexander Novikov authored at least 25 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Mathematical discoveries from program search with large language models.

Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M. Pawan Kumar, Emilien Dupont, Francisco J. R. Ruiz, Jordan S. Ellenberg, Pengming Wang, Omar Fawzi, Pushmeet Kohli, Alhussein Fawzi

Nat., January, 2024

Quantum Circuit Optimization with AlphaTensor.

Francisco J. R. Ruiz, Tuomas Laakkonen, Johannes Bausch, Matej Balog, Mohammadamin Barekatain, Francisco J. H. Heras, Alexander Novikov, Nathan Fitzpatrick, Bernardino Romera-Paredes, John van de Wetering, Alhussein Fawzi, Konstantinos Meichanetzidis, Pushmeet Kohli

CoRR, 2024

2022

A Generalist Agent.

Scott E. Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas

Trans. Mach. Learn. Res., 2022

Automatic Differentiation for Riemannian Optimization on Low-Rank Matrix and Tensor-Train Manifolds.

Alexander Novikov, Maxim V. Rakhuba, Ivan V. Oseledets

SIAM J. Sci. Comput., 2022

Discovering faster matrix multiplication algorithms with reinforcement learning.

Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J. R. Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, David Silver, Demis Hassabis, Pushmeet Kohli

Nat., 2022

2021

Benchmarks for Deep Off-Policy Evaluation.

Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Tensor Train Decomposition on TensorFlow (T3F).

Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan V. Oseledets

J. Mach. Learn. Res., 2020

Semi-supervised reward learning for offline reinforcement learning.

Ksenia Konyushkova, Konrad Zolna, Yusuf Aytar, Alexander Novikov, Scott E. Reed, Serkan Cabi, Nando de Freitas

CoRR, 2020

Offline Learning from Demonstrations and Unlabeled Experience.

Konrad Zolna, Alexander Novikov, Ksenia Konyushkova, Çaglar Gülçehre, Ziyu Wang, Yusuf Aytar, Misha Denil, Nando de Freitas, Scott E. Reed

CoRR, 2020

Hyperparameter Selection for Offline Reinforcement Learning.

Tom Le Paine, Cosmin Paduraru, Andrea Michi, Çaglar Gülçehre, Konrad Zolna, Alexander Novikov, Ziyu Wang, Nando de Freitas

CoRR, 2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning.

Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas

CoRR, 2020

Acme: A Research Framework for Distributed Reinforcement Learning.

Matt Hoffman, 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, Sergio Gómez Colmenarejo, Serkan Cabi, Çaglar Gülçehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas

CoRR, 2020

Scaling data-driven robotics with reward sketching and batch reinforcement learning.

Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott E. Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerík, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, Ziyu Wang

Proceedings of the Robotics: Science and Systems XVI, 2020

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning.

Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Critic Regularized Regression.

Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh Merel, Jost Tobias Springenberg, Scott E. Reed, Bobak Shahriari, Noah Y. Siegel, Çaglar Gülçehre, Nicolas Heess, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Task-Relevant Adversarial Imitation Learning.

Konrad Zolna, Scott E. Reed, Alexander Novikov, Sergio Gómez Colmenarejo, David Budden, Serkan Cabi, Misha Denil, Nando de Freitas, Ziyu Wang

Proceedings of the 4th Conference on Robot Learning, 2020

2019

Low-rank Riemannian eigensolver for high-dimensional Hamiltonians.

Maxim V. Rakhuba, Alexander Novikov, Ivan V. Oseledets

J. Comput. Phys., 2019

A Framework for Data-Driven Robotics.

Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott E. Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerík, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, Ziyu Wang

CoRR, 2019

2018

Expressive power of recurrent neural networks.

Valentin Khrulkov, Alexander Novikov, Ivan V. Oseledets

Proceedings of the 6th International Conference on Learning Representations, 2018

Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition.

Pavel Izmailov, Alexander Novikov, Dmitry Kropotov

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Exponential Machines.

Alexander Novikov, Mikhail Trofimov, Ivan V. Oseledets

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Tensor Train polynomial models via Riemannian optimization.

Alexander Novikov, Mikhail Trofimov, Ivan V. Oseledets

CoRR, 2016

Ultimate tensorization: compressing convolutional and FC layers alike.

Timur Garipov, Dmitry Podoprikhin, Alexander Novikov, Dmitry P. Vetrov

CoRR, 2016

2015

Tensorizing Neural Networks.

Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry P. Vetrov

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Putting MRFs on a Tensor Train.

Alexander Novikov, Anton Rodomanov, Anton Osokin, Dmitry P. Vetrov

Proceedings of the 31th International Conference on Machine Learning, 2014