| Generalizing across domains via cross-gradient training S Shankar, V Piratla, S Chakrabarti, S Chaudhuri, P Jyothi, S Sarawagi arXiv preprint arXiv:1804.10745, 2018 | 609 | 2018 |
| f4: Facebook's warm {BLOB} storage system S Muralidhar, W Lloyd, S Roy, C Hill, E Lin, W Liu, S Pan, S Shankar, ... 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2014 | 474 | 2014 |
| Optimizing for the future in non-stationary mdps Y Chandak, G Theocharous, S Shankar, M White, S Mahadevan, ... International Conference on Machine Learning, 1414-1425, 2020 | 76 | 2020 |
| Surprisingly easy hard-attention for sequence to sequence learning S Shankar, S Garg, S Sarawagi Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 51 | 2018 |
| Posterior attention models for sequence to sequence learning S Shankar, S Sarawagi ICLR, 2019 | 43 | 2019 |
| Multimodal fusion via cortical network inspired losses S Shankar Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 15 | 2022 |
| Labeled memory networks for online model adaptation S Shankar, S Sarawagi Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 13* | 2018 |
| Progressive fusion for multimodal integration S Shankar, L Thompson, M Fiterau arXiv preprint arXiv:2209.00302, 2022 | 9 | 2022 |
| Differential equation units: learning functional forms of activation functions from data S Shankar, MA Torkamani, A Rooshenas, P Wallis Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6030-6037, 2020 | 9* | 2020 |
| Off-policy evaluation for action-dependent non-stationary environments Y Chandak, S Shankar, N Bastian, B da Silva, E Brunskill, PS Thomas Advances in Neural Information Processing Systems 35, 9217-9232, 2022 | 8 | 2022 |
| Adaptive instrument design for indirect experiments Y Chandak, S Shankar, V Syrgkanis, E Brunskill The Twelfth International Conference on Learning Representations, 2023 | 7 | 2023 |
| High-confidence off-policy (or counterfactual) variance estimation Y Chandak, S Shankar, PS Thomas Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 6939-6947, 2021 | 7 | 2021 |
| Direct inference of effect of treatment (diet) for a cookieless world S Shankar, R Sinha, S Mitra, M Sinha, M Fiterau International Conference on Artificial Intelligence and Statistics, 1869-1887, 2023 | 5 | 2023 |
| Privacy aware experiments without cookies S Shankar, R Sinha, S Mitra, V Swaminathan, S Mahadevan, M Sinha Proceedings of the Sixteenth ACM International Conference on Web Search and …, 2023 | 5 | 2023 |
| Three-quarter sibling regression for denoising observational data S Shankar, D Sheldon, T Sun, J Pickering, TG Dietterich arXiv preprint arXiv:2101.00074, 2020 | 5 | 2020 |
| Recommended strategies for dietary modification S Kumanyika, S Shankar, P Mitchell, P Ganganna, SA Smith, L Thompson, ... Report of the Technical Advisory Panel on Dietary Modification. US …, 1990 | 4 | 1990 |
| Bosonic Random Walk Neural Networks for Graph Learning S Shankar, D Towsley Complex Networks & Their Applications X: Volume 2, Proceedings of the Tenth …, 2022 | 3* | 2022 |
| Neural Dependency Coding inspired Multimodal Fusion S Shankar arXiv preprint arXiv:2110.00385, 2021 | 3 | 2021 |
| Variational Boson Sampling S Shankar, D Towsley Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 2 | 2022 |
| Sibling Regression for Generalized Linear Models S Shankar, D Sheldon Machine Learning and Knowledge Discovery in Databases. Research Track …, 2021 | 2 | 2021 |
