| node2vec: Scalable feature learning for networks A Grover, J Leskovec Proceedings of the 22nd ACM SIGKDD international conference on Knowledge …, 2016 | 13128 | 2016 |
| Decision transformer: Reinforcement learning via sequence modeling L Chen, K Lu, A Rajeswaran, K Lee, A Grover, M Laskin, P Abbeel, ... Advances in neural information processing systems 34, 15084-15097, 2021 | 1658 | 2021 |
| Closed-loop optimization of fast-charging protocols for batteries with machine learning PM Attia*, A Grover*, N Jin, KA Severson, TM Markov, YH Liao, MH Chen, ... Nature 578 (7795), 397-402, 2020 | 806 | 2020 |
| Graphite: Iterative generative modeling of graphs A Grover, A Zweig, S Ermon Proceedings of the 36th International Conference on Machine Learning, 2434-2444, 2019 | 343 | 2019 |
| Pretrained transformers as universal computation engines K Lu, A Grover, P Abbeel, I Mordatch AAAI Conference on Artificial Intelligence, 2022 | 312 | 2022 |
| A deep hybrid model for weather forecasting A Grover, A Kapoor, E Horvitz Proceedings of the 21st ACM SIGKDD International Conference on Knowledge …, 2015 | 312 | 2015 |
| Climax: A foundation model for weather and climate T Nguyen, J Brandstetter, A Kapoor, JK Gupta, A Grover arXiv preprint arXiv:2301.10343, 2023 | 251* | 2023 |
| Online decision transformer Q Zheng, A Zhang, A Grover International Conference on Machine Learning, 2022 | 242 | 2022 |
| Permutation invariant graph generation via score-based generative modeling C Niu, Y Song, J Song, S Zhao, A Grover, S Ermon International Conference on Artificial Intelligence and Statistics, 4474-4484, 2020 | 242 | 2020 |
| Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models A Grover, M Dhar, S Ermon AAAI Conference on Artificial Intelligence, 2018 | 241* | 2018 |
| Learning controllable fair representations J Song, P Kalluri, A Grover, S Zhao, S Ermon Proceedings of the 22nd International Conference on Artificial Intelligence …, 2019 | 210 | 2019 |
| Stochastic optimization of sorting networks via continuous relaxations A Grover, E Wang, A Zweig, S Ermon International Conference on Learning Representations, 2019 | 174 | 2019 |
| Neural joint source-channel coding K Choi, K Tatwawadi, A Grover, T Weissman, S Ermon Proceedings of the 36th International Conference on Machine Learning, 1182-1192, 2019 | 156* | 2019 |
| Fair generative modeling via weak supervision K Choi, A Grover, T Singh, R Shu, S Ermon International Conference on Machine Learning, 1887-1898, 2020 | 148* | 2020 |
| Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting A Grover, J Song, A Agarwal, K Tran, A Kapoor, E Horvitz, S Ermon Advances in Neural Information Processing Systems, 11056--11068, 2019 | 144 | 2019 |
| Learning policy representations in multiagent systems A Grover, M Al-Shedivat, JK Gupta, Y Burda, H Edwards Proceedings of the 35th International Conference on Machine Learning, 1802-1811, 2018 | 144* | 2018 |
| Cyclip: Cyclic contrastive language-image pretraining S Goel, H Bansal, S Bhatia, R Rossi, V Vinay, A Grover Advances in Neural Information Processing Systems 35, 6704-6719, 2022 | 141 | 2022 |
| Frame averaging for invariant and equivariant network design O Puny, M Atzmon, H Ben-Hamu, EJ Smith, I Misra, A Grover, Y Lipman International Conference on Learning Representations, 2022 | 119 | 2022 |
| Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling T Nguyen, A Grover International Conference on Machine Learning, 2022 | 109 | 2022 |
| Modeling sparse deviations for compressed sensing using generative models M Dhar, A Grover, S Ermon Proceedings of the 35th International Conference on Machine Learning, 1214-1223, 2018 | 89 | 2018 |
