| Google vizier: A service for black-box optimization D Golovin, B Solnik, S Moitra, G Kochanski, J Karro, D Sculley Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 884 | 2017 |
| The hanabi challenge: A new frontier for ai research N Bard, JN Foerster, S Chandar, N Burch, M Lanctot, HF Song, E Parisotto, ... Artificial Intelligence 280, 103216, 2020 | 431 | 2020 |
| Autonomous navigation of stratospheric balloons using reinforcement learning MG Bellemare, S Candido, PS Castro, J Gong, MC Machado, S Moitra, ... Nature 588 (7836), 77-82, 2020 | 389 | 2020 |
| Dopamine: A research framework for deep reinforcement learning PS Castro, S Moitra, C Gelada, S Kumar, MG Bellemare arXiv preprint arXiv:1812.06110, 2018 | 294 | 2018 |
| Learning to fix build errors with graph2diff neural networks D Tarlow, S Moitra, A Rice, Z Chen, PA Manzagol, C Sutton, E Aftandilian Proceedings of the IEEE/ACM 42nd international conference on software …, 2020 | 79 | 2020 |
| Optimization of parameter values for machine-learned models DR Golovin, B Solnik, S Moitra, IIDW Sculley, GP Kochanski US Patent 12,026,612, 2024 | 48 | 2024 |
| PLUR: A unifying, graph-based view of program learning, understanding, and repair Z Chen, VJ Hellendoorn, P Lamblin, P Maniatis, PA Manzagol, D Tarlow, ... Advances in Neural Information Processing Systems 34, 23089-23101, 2021 | 33 | 2021 |
| Distributional reinforcement learning with linear function approximation MG Bellemare, N Le Roux, PS Castro, S Moitra The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 28 | 2019 |
| Optimization of parameters of a system, product, or process DR Golovin, B Solnik, S Moitra, IIDW Sculley US Patent 12,032,464, 2024 | 22 | 2024 |
| Bayesian optimization for a better dessert B Solnik, D Golovin, G Kochanski, JE Karro, S Moitra, D Sculley Proceedings of the 2017 NIPS Workshop on Bayesian Optimization, 2017 | 21 | 2017 |
| Bayesian optimization for a better dessert G Kochanski, D Golovin, J Karro, B Solnik, S Moitra, D Sculley NIPS, workshop on Bayesian optimization, 2017 | 19 | 2017 |
| Fast training of sparse graph neural networks on dense hardware M Balog, B van Merriënboer, S Moitra, Y Li, D Tarlow arXiv preprint arXiv:1906.11786, 2019 | 11 | 2019 |
| A minimal ligand binding pocket within a network of correlated mutations identified by multiple sequence and structural analysis of G protein coupled receptors S Moitra, KC Tirupula, J Klein-Seetharaman, CJ Langmead BMC biophysics 5, 1-17, 2012 | 8 | 2012 |
| Time-varying gaussian graphical models of molecular dynamics data NS Razavian, S Moitra, H Kamisetty, A Ramanathan, CJ Langmead Proceedings of 3DSIG, 2010 | 7 | 2010 |
| Analogies between structural and systems biology and systems-of-systems engineering in dynamic environments S Moitra, N Yanamala, O Tastan, I Singh, CJ Langmead, ... 2010 5th International Conference on System of Systems Engineering, 1-7, 2010 | 2 | 2010 |
| Optimizing sparse graph neural networks for dense hardware DS Tarlow, M Balog, B Van Merrienboer, Y Li, S Moitra US Patent 11,562,239, 2023 | 1 | 2023 |
| Optimization of Parameters of a System, Product, or Process DR Golovin, B Solnik, S Moitra, IIDW Sculley US Patent App. 18/347,386, 2023 | | 2023 |
| Optimization of Parameter Values for Machine-Learned Models DR Golovin, B Solnik, S Moitra, IIDW Sculley, GP Kochanski US Patent App. 18/347,406, 2023 | | 2023 |
| Feature Learning and Graphical Models for Protein Sequences S Moitra Carnegie Mellon University, 2015 | | 2015 |
| Dopamine: A framework for flexible Reinforcement Learning research PS Castro, S Moitra, C Gelada, S Kumar, MG Bellemare | | |
