| Neo: A learned query optimizer R Marcus, P Negi, H Mao, C Zhang, M Alizadeh, T Kraska, ... arXiv preprint arXiv:1904.03711, 2019 | 612 | 2019 |
| Bao: Making learned query optimization practical R Marcus, P Negi, H Mao, N Tatbul, M Alizadeh, T Kraska Proceedings of the 2021 International Conference on Management of Data, 1275 …, 2021 | 394 | 2021 |
| High throughput cryptocurrency routing in payment channel networks V Sivaraman, SB Venkatakrishnan, K Ruan, P Negi, L Yang, R Mittal, ... 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2020 | 219 | 2020 |
| Evaluating end-to-end optimization for data analytics applications in weld S Palkar, S Amarasinghe, S Madden, M Zaharia, J Thomas, D Narayanan, ... Proceedings of the VLDB Endowment 11 (9), 2018 | 123 | 2018 |
| Flow-loss: Learning cardinality estimates that matter P Negi, R Marcus, A Kipf, H Mao, N Tatbul, T Kraska, M Alizadeh arXiv preprint arXiv:2101.04964, 2021 | 120 | 2021 |
| Park: An open platform for learning-augmented computer systems H Mao, P Negi, A Narayan, H Wang, J Yang, H Wang, R Marcus, ... Advances in Neural Information Processing Systems 32, 2019 | 120 | 2019 |
| Robust query driven cardinality estimation under changing workloads P Negi, Z Wu, A Kipf, N Tatbul, R Marcus, S Madden, T Kraska, ... Proceedings of the VLDB Endowment 16 (6), 2023 | 96 | 2023 |
| FactorJoin: a new cardinality estimation framework for join queries Z Wu, P Negi, M Alizadeh, T Kraska, S Madden Proceedings of the ACM on Management of Data 1 (1), 1-27, 2023 | 81 | 2023 |
| Steering query optimizers: A practical take on big data workloads P Negi, M Interlandi, R Marcus, M Alizadeh, T Kraska, M Friedman, ... Proceedings of the 2021 international conference on management of data, 2557 …, 2021 | 66 | 2021 |
| Bao: Learning to steer query optimizers R Marcus, P Negi, H Mao, N Tatbul, M Alizadeh, T Kraska arXiv preprint arXiv:2004.03814, 2020 | 60 | 2020 |
| Cost-guided cardinality estimation: Focus where it matters P Negi, R Marcus, H Mao, N Tatbul, T Kraska, M Alizadeh 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW …, 2020 | 48 | 2020 |
| Stage: Query execution time prediction in amazon redshift Z Wu, R Marcus, Z Liu, P Negi, V Nathan, P Pfeil, G Saxena, M Rahman, ... Companion of the 2024 International Conference on Management of Data, 280-294, 2024 | 35 | 2024 |
| K-means++ vs. Behavioral Biometrics: One Loop to Rule Them All. P Negi, P Sharma, V Jain, B Bahmani NDSS, 2018 | 28 | 2018 |
| Neo: A Learned query optimizer. PVLDB 12, 11 (2018), 1705–1718 R Marcus, P Negi, H Mao, C Zhang, M Alizadeh, T Kraska, ... | 10 | 1904 |
| Adversarial machine learning against keystroke dynamics P Negi, A Sharma, C Robustness Stanford, 2017 | 8 | 2017 |
| Unshackling database benchmarking from synthetic workloads P Negi, L Bindschaedler, M Alizadeh, T Kraska, J Leeka, A Gruenheid, ... 2023 IEEE 39th International Conference on Data Engineering (ICDE), 3659-3662, 2023 | 7 | 2023 |
| OS Pre-trained Transformer: Predicting Query Latencies across Changing System Contexts P Negi, Z Wu, A Nasr-Esfahany, H Sharma, M Alizadeh, T Kraska, ... Submission, 2024 | 3 | 2024 |
| Weld: Rethinking the interface between data-intensive libraries S Palkar, JJ Thomas, D Narayanan, P Thaker, R Palamuttam, P Negi, ... Proceedings of the 2018 International Conference on Management of Data …, 0 | 1 | |
| Machine Learning for Out of Distribution Database Workloads P Negi Massachusetts Institute of Technology, 2024 | | 2024 |
| Some Cardinality Estimates are More Equal than Others P Negi Massachusetts Institute of Technology, 2022 | | 2022 |
