Alexander Novikov

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 31

Top Topics

Gaussian Process

Reinforcement Learning

Policy Evaluation

Top Venues

Publications

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
Mathematical discoveries from program search with large language models. Nat. 625 (7995) (2024)
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
Quantum Circuit Optimization with AlphaTensor. CoRR (2024)
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
A Generalist Agent. Trans. Mach. Learn. Res. 2022 (2022)
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
Discovering faster matrix multiplication algorithms with reinforcement learning. Nat. 610 (7930) (2022)
Alexander Novikov, Maxim Rakhuba, Ivan Oseledets
Automatic Differentiation for Riemannian Optimization on Low-Rank Matrix and Tensor-Train Manifolds. SIAM J. Sci. Comput. 44 (2) (2022)
Scott E. Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez 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
A Generalist Agent. CoRR (2022)
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, Thomas Paine
Benchmarks for Deep Off-Policy Evaluation. ICLR (2021)
Alexander Novikov, Maxim V. Rakhuba, Ivan V. Oseledets
Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds. CoRR (2021)
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
Benchmarks for Deep Off-Policy Evaluation. CoRR (2021)
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
Scaling data-driven robotics with reward sketching and batch reinforcement learning. Robotics: Science and Systems (2020)
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
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. NeurIPS (2020)
Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan V. Oseledets
Tensor Train Decomposition on TensorFlow (T3F). J. Mach. Learn. Res. 21 (2020)
Tom Le Paine, Cosmin Paduraru, Andrea Michi, Çaglar Gülçehre, Konrad Zolna, Alexander Novikov, Ziyu Wang, Nando de Freitas
Hyperparameter Selection for Offline Reinforcement Learning. CoRR (2020)
Konrad Zolna, Alexander Novikov, Ksenia Konyushkova, Çaglar Gülçehre, Ziyu Wang, Yusuf Aytar, Misha Denil, Nando de Freitas, Scott E. Reed
Offline Learning from Demonstrations and Unlabeled Experience. CoRR (2020)
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
RL Unplugged: Benchmarks for Offline Reinforcement Learning. CoRR (2020)
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
Critic Regularized Regression. NeurIPS (2020)
Ziyu Wang, Alexander Novikov, Konrad Zolna, Jost Tobias Springenberg, Scott E. Reed, Bobak Shahriari, Noah Y. Siegel, Josh Merel, Çaglar Gülçehre, Nicolas Heess, Nando de Freitas
Critic Regularized Regression. CoRR (2020)
Konrad Zolna, Scott E. Reed, Alexander Novikov, Sergio Gómez Colmenarejo, David Budden, Serkan Cabi, Misha Denil, Nando de Freitas, Ziyu Wang
Task-Relevant Adversarial Imitation Learning. CoRL (2020)
Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal 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
Acme: A Research Framework for Distributed Reinforcement Learning. CoRR (2020)
Ksenia Konyushkova, Konrad Zolna, Yusuf Aytar, Alexander Novikov, Scott E. Reed, Serkan Cabi, Nando de Freitas
Semi-supervised reward learning for offline reinforcement learning. CoRR (2020)
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
A Framework for Data-Driven Robotics. CoRR (2019)
Konrad Zolna, Scott E. Reed, Alexander Novikov, Sergio Gomez Colmenarejo, David Budden, Serkan Cabi, Misha Denil, Nando de Freitas, Ziyu Wang
Task-Relevant Adversarial Imitation Learning. CoRR (2019)
Maxim V. Rakhuba, Alexander Novikov, Ivan V. Oseledets
Low-rank Riemannian eigensolver for high-dimensional Hamiltonians. J. Comput. Phys. 396 (2019)
Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan V. Oseledets
Tensor Train decomposition on TensorFlow (T3F). CoRR (2018)
Pavel Izmailov, Alexander Novikov, Dmitry Kropotov
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition. AISTATS (2018)
Valentin Khrulkov, Alexander Novikov, Ivan V. Oseledets
Expressive power of recurrent neural networks. ICLR (Poster) (2018)
Alexander Novikov, Mikhail Trofimov, Ivan V. Oseledets
Exponential Machines. ICLR (Workshop) (2017)
Pavel Izmailov, Alexander Novikov, Dmitry Kropotov
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition. CoRR (2017)
Valentin Khrulkov, Alexander Novikov, Ivan V. Oseledets
Expressive power of recurrent neural networks. CoRR (2017)
Timur Garipov, Dmitry Podoprikhin, Alexander Novikov, Dmitry P. Vetrov
Ultimate tensorization: compressing convolutional and FC layers alike. CoRR (2016)
Alexander Novikov, Mikhail Trofimov, Ivan V. Oseledets
Tensor Train polynomial models via Riemannian optimization. CoRR (2016)
Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry P. Vetrov
Tensorizing Neural Networks. NIPS (2015)
Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry P. Vetrov
Tensorizing Neural Networks. CoRR (2015)
Alexander Novikov, Anton Rodomanov, Anton Osokin, Dmitry P. Vetrov
Putting MRFs on a Tensor Train. ICML (2014)