| 2025 | ICLR | E(3)-equivariant models cannot learn chirality: Field-based molecular generation. | Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lhdesmki |
| 2025 | ICLR | Diffusion Models as Cartoonists: The Curious Case of High Density Regions. | Rafal Karczewski, Markus Heinonen, Vikas Garg |
| 2025 | ICLR | Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models. | Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg |
| 2025 | ICLR | When do GFlowNets learn the right distribution? | Tiago da Silva, Rodrigo Barreto Alves, Eliezer de Souza da Silva, Amauri H. Souza, Vikas Garg, Samuel Kaski, Diego Mesquita |
| 2025 | ICLR | Generalization and Distributed Learning of GFlowNets. | Tiago Silva, Amauri H. Souza, Omar Rivasplata, Vikas Garg, Samuel Kaski, Diego Mesquita |
| 2025 | ICLR | Robust Simulation-Based Inference under Missing Data via Neural Processes. | Yogesh Verma, Ayush Bharti, Vikas Garg |
| 2024 | CHI | Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces. | Yue Jiang, Changkong Zhou, Vikas Garg, Antti Oulasvirta |
| 2024 | ICLR | ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs. | Yogesh Verma, Markus Heinonen, Vikas Garg |
| 2024 | ICML | On the Generalization of Equivariant Graph Neural Networks. | Rafal Karczewski, Amauri H. Souza, Vikas Garg |
| 2024 | ICML | Topological Neural Networks go Persistent, Equivariant, and Continuous. | Yogesh Verma, Amauri H. Souza, Vikas Garg |
| 2023 | ICML | AbODE: Ab initio antibody design using conjoined ODEs. | Yogesh Verma, Markus Heinonen, Vikas Garg |