Yulia Rubanova

According to our database, Yulia Rubanova authored at least 16 papers between 2018 and 2024.

Collaborative distances:

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

2024

Scaling Face Interaction Graph Networks to Real World Scenes.

Tatiana Lopez-Guevara, Yulia Rubanova, William F. Whitney, Tobias Pfaff, Kimberly L. Stachenfeld, Kelsey R. Allen

CoRR, 2024

2023

Learning 3D Particle-based Simulators from RGB-D Videos.

William F. Whitney, Tatiana Lopez-Guevara, Tobias Pfaff, Yulia Rubanova, Thomas Kipf, Kimberly L. Stachenfeld, Kelsey R. Allen

CoRR, 2023

Learning rigid dynamics with face interaction graph networks.

Kelsey R. Allen, Yulia Rubanova, Tatiana Lopez-Guevara, William Whitney, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Tobias Pfaff

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

A Generalist Neural Algorithmic Learner.

Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Joseph Dudzik, Matko Bosnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, Petar Velickovic

Proceedings of the Learning on Graphs Conference, 2022

Constraint-based graph network simulator.

Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Peter W. Battaglia

Proceedings of the International Conference on Machine Learning, 2022

Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond.

Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia

Proceedings of the Tenth International Conference on Learning Representations, 2022

Graph network simulators can learn discontinuous, rigid contact dynamics.

Kelsey R. Allen, Tatiana Lopez-Guevara, Yulia Rubanova, Kimberly L. Stachenfeld, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Tobias Pfaff

Proceedings of the Conference on Robot Learning, 2022

2021

Very Deep Graph Neural Networks Via Noise Regularisation.

Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia

CoRR, 2021

2020

Continuous-time Latent-variable Models for Time Series.

Yulia Rubanova

PhD thesis, 2020

The evolutionary history of 2,658 cancers.

Moritz Gerstung, Clemency Jolly, Ignaty Leshchiner, Stefan C. Dentro, Santiago Gonzalez, Thomas J. Mitchell, Yulia Rubanova, Pavana Anur, Kaixian Yu, Maxime Tarabichi, Amit G. Deshwar, Jeff Wintersinger, Kortine Kleinheinz, Ignacio Vázquez-García, Kerstin Haase, Lara Jerman, Subhajit Sengupta, Geoff MacIntyre, Salem Malikic, Nilgun Donmez, Dimitri G. Livitz, Marek Cmero, Jonas Demeulemeester, Steven E. Schumacher, Yu Fan, Xiaotong Yao, Juhee Lee, Matthias Schlesner, Paul C. Boutros, David D. L. Bowtell, Hongtu Zhu, Gad Getz, Marcin Imielinski, Rameen Beroukhim, Süleyman Cenk Sahinalp, Yuan Ji, Martin Peifer, Florian Markowetz, Ville Mustonen, Ke Yuan, Wenyi Wang, Quaid D. Morris, David J. Adams, Peter J. Campbell, Shaolong Cao, Elizabeth L. Christie, Yupeng Cun, Kevin J. Dawson, Ruben M. Drews, Roland Eils, Matthew Fittall, Dale W. Garsed, Gavin Ha, Henry Lee-Six, Iñigo Martincorena, Layla Oesper, Myron Peto, Benjamin J. Raphael, Daniel T. Rosebrock, Adriana Salcedo, Ruian Shi, Seung Jun Shin, Oliver Spiro, Lincoln D. Stein, Shankar Vembu, David A. Wheeler, Tsun-Po Yang, Paul T. Spellman, David C. Wedge, Peter Van Loo

Nat., 2020

Amortized Bayesian Optimization over Discrete Spaces.

Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

TrackSigFreq: Subclonal Reconstructions Based on Mutation Signatures and Allele Frequencies.

Caitlin F. Harrigan, Yulia Rubanova, Quaid Morris, Alina Selega

Proceedings of the Pacific Symposium on Biocomputing 2020, 2020

2019

Latent ODEs for Irregularly-Sampled Time Series.

Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud

CoRR, 2019

Latent Ordinary Differential Equations for Irregularly-Sampled Time Series.

Yulia Rubanova, Tian Qi Chen, David Duvenaud

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Neural Ordinary Differential Equations.

Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Reconstructing evolutionary trajectories of mutations in cancer.

Yulia Rubanova, Ruian Shi, Roujia Li, Jeff Wintersinger, Amit G. Deshwar, Nil Sahin, Quaid Morris

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