| 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 | 491 | 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 | 294 | 2023 |
| A decoder-only foundation model for time-series forecasting A Das, W Kong, R Sen, Y Zhou arXiv preprint arXiv:2310.10688, 2023 | 179 | 2023 |
| Model-powered conditional independence test R Sen, AT Suresh, K Shanmugam, AG Dimakis, S Shakkottai Advances in neural information processing systems 30, 2017 | 107 | 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 | 89 | 2017 |
| Regret of queueing bandits S Krishnasamy, R Sen, R Johari, S Shakkottai Advances in Neural Information Processing Systems, 1669-1677, 2016 | 74 | 2016 |
| 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 | 67 | 2021 |
| Multi-fidelity black-box optimization with hierarchical partitions R Sen, K Kandasamy, S Shakkottai International conference on machine learning, 4538-4547, 2018 | 64 | 2018 |
| 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 | 56 | 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 | 39 | 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 | 27* | 2019 |
| Importance weighted generative networks M Diesendruck, ER Elenberg, R Sen, GW Cole, S Shakkottai, ... Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019 | 25 | 2019 |
| Hierarchically regularized deep forecasting B Paria, R Sen, A Ahmed, A Das arXiv preprint arXiv:2106.07630, 2021 | 21 | 2021 |
| 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 | 19 | 2018 |
| 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 | 16 | 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 | 14 | 2022 |
| Dirichlet proportions model for hierarchically coherent probabilistic forecasting A Das, W Kong, B Paria, R Sen Uncertainty in Artificial Intelligence, 518-528, 2023 | 13* | 2023 |
Abhimanyu DasGoogle確認したメール アドレス: google.com