Reconstructing evolutionary trajectories of mutations in cancer. - Yulia Rubanova; Ruian Shi; Roujia Li; Jeff Wintersinger; Amit G. Deshwar; Nil Sahin; Quaid Morris (ICLR (Workshop), 2018)

Neural Ordinary Differential Equations. - Tian Qi Chen; Yulia Rubanova; Jesse Bettencourt; David Duvenaud (NeurIPS, 2018)

Latent Ordinary Differential Equations for Irregularly-Sampled Time Series. - Yulia Rubanova; Tian Qi Chen; David Duvenaud (NeurIPS, 2019)

Latent ODEs for Irregularly-Sampled Time Series. - Yulia Rubanova; Ricky T. Q. Chen; David Duvenaud (CoRR, 2019)

TrackSigFreq: Subclonal Reconstructions Based on Mutation Signatures and Allele Frequencies. - Caitlin F. Harrigan; Yulia Rubanova; Quaid Morris; Alina Selega (PSB, 2020)

Amortized Bayesian Optimization over Discrete Spaces. - Kevin Swersky; Yulia Rubanova; David Dohan; Kevin Murphy 002 (UAI, 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; et al. (Nat., 2020)

Continuous-time Latent-variable Models for Time Series. - Yulia Rubanova (2020)

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)

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 (CoRL, 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 (ICLR, 2022)

Constraint-based graph network simulator. - Yulia Rubanova; Alvaro Sanchez-Gonzalez; Tobias Pfaff; Peter W. Battaglia (ICML, 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; et al. (LoG, 2022)

Learning rigid dynamics with face interaction graph networks. - Kelsey R. Allen; Yulia Rubanova; Tatiana Lopez-Guevara; William Whitney 001; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Tobias Pfaff (ICLR, 2023)

Learning 3D Particle-based Simulators from RGB-D Videos. - William F. Whitney; Tatiana Lopez-Guevara; Tobias Pfaff; Yulia Rubanova; Thomas Kipf; Kim Stachenfeld; Kelsey R. Allen (ICLR, 2024)

Learning rigid-body simulators over implicit shapes for large-scale scenes and vision. - Yulia Rubanova; Tatiana Lopez-Guevara; Kelsey R. Allen; Will Whitney; Kimberly L. Stachenfeld; Tobias Pfaff (NeurIPS, 2024)

Moving Off-the-Grid: Scene-Grounded Video Representations. - Sjoerd van Steenkiste; Daniel Zoran; Yi Yang 007; Yulia Rubanova; Rishabh Kabra; Carl Doersch; Dilara Gokay; Joseph Heyward; Etienne Pot; Klaus Greff; Drew A. Hudson; Thomas Keck; et al. (NeurIPS, 2024)

Neural Assets: 3D-Aware Multi-Object Scene Synthesis with Image Diffusion Models. - Ziyi Wu; Yulia Rubanova; Rishabh Kabra; Drew A. Hudson; Igor Gilitschenski; Yusuf Aytar; Sjoerd van Steenkiste; Kelsey R. Allen; Thomas Kipf (NeurIPS, 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)

Direct Motion Models for Assessing Generated Videos. - Kelsey R. Allen; Carl Doersch; Guangyao Zhou; Mohammed Suhail; Danny Driess; Ignacio Rocco; Yulia Rubanova; Thomas Kipf; Mehdi S. M. Sajjadi; Kevin Murphy; João Carreira 001; Sjoerd van Steenkiste (CoRR, 2025)