| Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting R Sen, HF Yu, IS Dhillon Advances in neural information processing systems 32, 2019 | 434 | 2019 |
| Long-term forecasting with tide: Time-series dense encoder A Das, W Kong, A Leach, S Mathur, R Sen, R Yu arXiv preprint arXiv:2304.08424, 2023 | 159 | 2023 |
| Model-powered conditional independence test R Sen, AT Suresh, K Shanmugam, AG Dimakis, S Shakkottai Advances in neural information processing systems 30, 2017 | 101 | 2017 |
| Identifying best interventions through online importance sampling R Sen, K Shanmugam, AG Dimakis, S Shakkottai Proceedings of the 34th International Conference on Machine Learning, PMLR …, 2017 | 82 | 2017 |
| Regret of queueing bandits S Krishnasamy, R Sen, R Johari, S Shakkottai Advances in Neural Information Processing Systems, 1669-1677, 2016 | 69 | 2016 |
| Multi-fidelity black-box optimization with hierarchical partitions R Sen, K Kandasamy, S Shakkottai International conference on machine learning, 4538-4547, 2018 | 59 | 2018 |
| Learning unknown service rates in queues: A multiarmed bandit approach S Krishnasamy, R Sen, R Johari, S Shakkottai Operations research 69 (1), 315-330, 2021 | 58 | 2021 |
| A decoder-only foundation model for time-series forecasting A Das, W Kong, R Sen, Y Zhou arXiv preprint arXiv:2310.10688, 2023 | 57 | 2023 |
| Contextual Bandits with Latent Confounders: An NMF Approach R Sen, K Shanmugam, M Kocaoglu, A Dimakis, S Shakkottai 20th International Conference on Artificial Intelligence and Statistics 54, 2017 | 55 | 2017 |
| Blocking bandits S Basu, R Sen, S Sanghavi, S Shakkottai arXiv preprint arXiv:1907.11975, 2019 | 45 | 2019 |
| Comparison between three tuning methods of PID control for high precision positioning stage R Sen, C Pati, S Dutta, R Sen Mapan 30 (1), 65-70, 2015 | 37 | 2015 |
| Top-k extreme contextual bandits with arm hierarchy R Sen, A Rakhlin, L Ying, R Kidambi, D Foster, DN Hill, IS Dhillon International Conference on Machine Learning, 9422-9433, 2021 | 30 | 2021 |
| Noisy blackbox optimization using multi-fidelity queries: A tree search approach R Sen, K Kandasamy, S Shakkottai The 22nd international conference on artificial intelligence and statistics …, 2019 | 25* | 2019 |
| Importance weighted generative networks M Diesendruck, ER Elenberg, R Sen, GW Cole, S Shakkottai, ... Machine Learning and Knowledge Discovery in Databases: European Conference …, 2020 | 23 | 2020 |
| Mimic and classify: A meta-algorithm for conditional independence testing R Sen, K Shanmugam, H Asnani, A Rahimzamani, S Kannan arXiv preprint arXiv:1806.09708, 2018 | 18 | 2018 |
| Hierarchically regularized deep forecasting B Paria, R Sen, A Ahmed, A Das arXiv preprint arXiv:2106.07630, 2021 | 17 | 2021 |
| Contextual bandits with stochastic experts R Sen, K Shanmugam, S Shakkottai International Conference on Artificial Intelligence and Statistics, 852-861, 2018 | 16 | 2018 |
| On learning mixture of linear regressions in the non-realizable setting S Pal, A Mazumdar, R Sen, A Ghosh International Conference on Machine Learning, 17202-17220, 2022 | 13 | 2022 |
| Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model P Awasthi, A Das, W Kong, R Sen Advances in Neural Information Processing Systems 35, 862-873, 2022 | 11* | 2022 |
| Session-aware query auto-completion using extreme multi-label ranking N Yadav, R Sen, DN Hill, A Mazumdar, IS Dhillon Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 11 | 2021 |
Abhimanyu DasGoogleVerified email at google.com
