| Fetchsgd: Communication-efficient federated learning with sketching D Rothchild, A Panda, E Ullah, N Ivkin, I Stoica, V Braverman, J Gonzalez, ... International Conference on Machine Learning, 8253-8265, 2020 | 388 | 2020 |
| Communication-efficient distributed SGD with sketching N Ivkin, D Rothchild, E Ullah, I Stoica, R Arora Advances in Neural Information Processing Systems 32, 2019 | 213 | 2019 |
| Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration S De, A Mukherjee, E Ullah arXiv preprint arXiv:1807.06766, 2018 | 103 | 2018 |
| Machine unlearning via algorithmic stability E Ullah, T Mai, A Rao, RA Rossi, R Arora Conference on Learning Theory, 4126-4142, 2021 | 82 | 2021 |
| Streaming Kernel PCA with Random Features E Ullah, P Mianjy, TV Marinov, R Arora Advances in Neural Information Processing Systems 31, 2018 | 40 | 2018 |
| Convergence guarantees for RMSProp and ADAM in non-convex optimization and their comparison to Nesterov acceleration on autoencoders A Basu, S De, A Mukherjee, E Ullah arXiv preprint arXiv:1807.06766, 28, 2018 | 36 | 2018 |
| Faster rates of convergence to stationary points in differentially private optimization R Arora, R Bassily, T González, CA Guzmán, M Menart, E Ullah International Conference on Machine Learning, 1060-1092, 2023 | 20 | 2023 |
| Differentially private generalized linear models revisited R Arora, R Bassily, C Guzmán, M Menart, E Ullah Advances in neural information processing systems 35, 22505-22517, 2022 | 17 | 2022 |
| Adversarial robustness is at odds with lazy training Y Wang, E Ullah, P Mianjy, R Arora Advances in Neural Information Processing Systems 35, 6505-6516, 2022 | 10 | 2022 |
| Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration. arXiv 2018 S De, A Mukherjee, E Ullah arXiv preprint arXiv:1807.06766, 2018 | 10 | 2018 |
| Improved algorithms for time decay streams V Braverman, H Lang, E Ullah, S Zhou arXiv preprint arXiv:1907.07574, 2019 | 8 | 2019 |
| Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration, 2018 S De, A Mukherjee, E Ullah arXiv preprint arXiv:1807.06766 42, 0 | 8 | |
| Optimistic rates for multi-task representation learning A Watkins, E Ullah, T Nguyen-Tang, R Arora Advances in Neural Information Processing Systems 36, 2207-2251, 2023 | 5 | 2023 |
| Private federated learning with autotuned compression E Ullah, CA Choquette-Choo, P Kairouz, S Oh International Conference on Machine Learning, 34668-34708, 2023 | 5 | 2023 |
| Private stochastic convex optimization: efficient algorithms for non-smooth objectives R Arora, TV Marinov, E Ullah arXiv preprint arXiv:2002.09609, 2020 | 3 | 2020 |
| From adaptive query release to machine unlearning E Ullah, R Arora International Conference on Machine Learning, 34642-34667, 2023 | 2 | 2023 |
| Generalization bounds for kernel canonical correlation analysis E Ullah, R Arora Transactions on machine learning research, 2023 | 2 | 2023 |
| Clustering using Approximate Nearest Neighbour Oracles E Ullah, H Lang, R Arora, V Braverman Transactions on Machine Learning Research, 2022 | 2 | 2022 |
| Machine unlearning and retraining of a machine learning model based on a modified training dataset E Ullah, AB Rao, T Mai, RA Rossi US Patent App. 17/451,260, 2023 | 1 | 2023 |
| Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates M Menart, E Ullah, R Arora, R Bassily, C Guzmán International Conference on Algorithmic Learning Theory, 868-906, 2024 | | 2024 |
Raman AroraDepartment of Computer Science, Johns Hopkins UniversityVerified email at cs.jhu.edu
