| Overview and importance of data quality for machine learning tasks A Jain, H Patel, L Nagalapatti, N Gupta, S Mehta, S Guttula, S Mujumdar, ... Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 342 | 2020 |
| Outlier resistant unsupervised deep architectures for attributed network embedding S Bandyopadhyay, L N, SV Vivek, MN Murty Proceedings of the 13th international conference on web search and data …, 2020 | 131 | 2020 |
| Outlier aware network embedding for attributed networks S Bandyopadhyay, N Lokesh, MN Murty Proceedings of the AAAI conference on artificial intelligence 33 (01), 12-19, 2019 | 107 | 2019 |
| Game of gradients: Mitigating irrelevant clients in federated learning L Nagalapatti, R Narayanam Proceedings of the AAAI Conference on Artificial Intelligence 35 (10), 9046-9054, 2021 | 89 | 2021 |
| Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets N Gupta, H Patel, S Afzal, N Panwar, RS Mittal, S Guttula, A Jain, ... arXiv preprint arXiv:2108.05935, 2021 | 48 | 2021 |
| Data augmentation for fairness in personal knowledge base population LS Vannur, B Ganesan, L Nagalapatti, H Patel, MN Tippeswamy Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2021 …, 2021 | 29* | 2021 |
| Is your data relevant?: Dynamic selection of relevant data for federated learning L Nagalapatti, RS Mittal, R Narayanam Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7859-7867, 2022 | 24 | 2022 |
| A Data-centric AI Framework for Automating Exploratory Data Analysis and Data Quality Tasks H Patel, S Guttula, N Gupta, S Hans, RS Mittal, L N ACM Journal of Data and Information Quality 15 (4), 1-26, 2023 | 15 | 2023 |
| Ranking data slices for ML model validation: A shapley value approach E Farchi, R Narayanam, L Nagalapatti 2021 IEEE 37th International Conference on Data Engineering (ICDE), 1937-1942, 2021 | 10 | 2021 |
| Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets. arXiv 2021 N Gupta, H Patel, S Afzal, N Panwar, RS Mittal, S Guttula, A Jain, ... arXiv preprint arXiv:2108.05935, 2023 | 6 | 2023 |
| Continuous treatment effect estimation using gradient interpolation and kernel smoothing L Nagalapatti, A Iyer, A De, S Sarawagi Proceedings of the AAAI Conference on Artificial Intelligence 38 (13), 14397 …, 2024 | 5 | 2024 |
| Machine-learning model retraining detection RS Mittal, L Nagalapatti, N Gupta, H Patel US Patent App. 17/036,843, 2022 | 3 | 2022 |
| Gradient Coreset for Federated Learning D Sivasubramanian, L Nagalapatti, R Iyer, G Ramakrishnan Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 1 | 2024 |
| Federated machine learning based on partially secured spatio-temporal data L Nagalapatti, S Bandyopadhyay, RS Mittal, R Narayanam US Patent App. 17/545,573, 2023 | 1 | 2023 |
| Quality assessment of machine-learning model dataset H Patel, L Nagalapatti, N Panwar, N Gupta, RS Mittal, S Mehta, ... US Patent App. 17/035,111, 2022 | 1 | 2022 |
| Tab-Shapley: Identifying Top-k Tabular Data Quality Insights M Padala, L Nagalapatti, A Tyagi, R Narayanam, SK Saini arXiv preprint arXiv:2501.06685, 2025 | | 2025 |
| PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect L Nagalapatti, P Singhal, A Ghosh, S Sarawagi arXiv preprint arXiv:2406.03864, 2024 | | 2024 |
| Generating task-specific training data L Nagalapatti, RS Mittal, S Bandyopadhyay, R Narayanam US Patent 11,983,238, 2024 | | 2024 |
| Training sample set generation from imbalanced data in view of user goals RS Mittal, L Nagalapatti, H Patel, N Gupta US Patent 11,836,219, 2023 | | 2023 |
| Federated learning data source selection RS Mittal, R Narayanam, L Nagalapatti, S Mehta US Patent App. 17/509,507, 2023 | | 2023 |
Ramasuri NarayanamAdobe Research, India確認したメール アドレス: adobe.com