| On the (in) fidelity and sensitivity of explanations CK Yeh, CY Hsieh, A Suggala, DI Inouye, PK Ravikumar Advances in neural information processing systems 32, 2019 | 441 | 2019 |
| Robust estimation via robust gradient estimation A Prasad, AS Suggala, S Balakrishnan, P Ravikumar Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2020 | 241 | 2020 |
| Protonn: Compressed and accurate knn for resource-scarce devices C Gupta, AS Suggala, A Goyal, HV Simhadri, B Paranjape, A Kumar, ... International conference on machine learning, 1331-1340, 2017 | 202 | 2017 |
| Revisiting adversarial risk AS Suggala, A Prasad, V Nagarajan, P Ravikumar The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 61* | 2019 |
| Online non-convex learning: Following the perturbed leader is optimal AS Suggala, P Netrapalli Algorithmic Learning Theory, 845-861, 2020 | 59 | 2020 |
| Connecting optimization and regularization paths A Suggala, A Prasad, PK Ravikumar Advances in Neural Information Processing Systems 31, 2018 | 58 | 2018 |
| Adaptive hard thresholding for near-optimal consistent robust regression AS Suggala, K Bhatia, P Ravikumar, P Jain Conference on Learning Theory, 2892-2897, 2019 | 48 | 2019 |
| Vector-space Markov random fields via exponential families W Tansey, OHM Padilla, AS Suggala, P Ravikumar International Conference on Machine Learning, 684-692, 2015 | 27 | 2015 |
| Boosted cvar classification R Zhai, C Dan, A Suggala, JZ Kolter, P Ravikumar Advances in Neural Information Processing Systems 34, 21860-21871, 2021 | 22 | 2021 |
| The expxorcist: Nonparametric graphical models via conditional exponential densities A Suggala, M Kolar, PK Ravikumar Advances in neural information processing systems 30, 2017 | 19 | 2017 |
| Follow the perturbed leader: Optimism and fast parallel algorithms for smooth minimax games A Suggala, P Netrapalli Advances in Neural Information Processing Systems 33, 22316-22326, 2020 | 17 | 2020 |
| Building robust ensembles via margin boosting D Zhang, H Zhang, A Courville, Y Bengio, P Ravikumar, AS Suggala International Conference on Machine Learning, 26669-26692, 2022 | 16 | 2022 |
| Ordinal graphical models: A tale of two approaches AS Suggala, E Yang, P Ravikumar International conference on machine learning, 3260-3269, 2017 | 14 | 2017 |
| Efficient bandit convex optimization: Beyond linear losses AS Suggala, P Ravikumar, P Netrapalli Conference on Learning Theory, 4008-4067, 2021 | 10 | 2021 |
| Latent feature lasso IEH Yen, WC Lee, SE Chang, AS Suggala, SD Lin, P Ravikumar International Conference on Machine Learning, 3949-3957, 2017 | 10 | 2017 |
| Generalized boosting A Suggala, B Liu, P Ravikumar Advances in neural information processing systems 33, 8787-8797, 2020 | 8 | 2020 |
| Resource-efficient machine learning P Jain, C Gupta, AS Suggala, A Goyal, H Simhadri US Patent App. 15/623,661, 2018 | 8 | 2018 |
| Optimal algorithms for latent bandits with cluster structure S Pal, AS Suggala, K Shanmugam, P Jain International Conference on Artificial Intelligence and Statistics, 7540-7577, 2023 | 6 | 2023 |
| Label robust and differentially private linear regression: computational and statistical efficiency X Liu, P Jain, W Kong, S Oh, A Suggala Advances in Neural Information Processing Systems 36, 2024 | 5* | 2024 |
| Stochastic re-weighted gradient descent via distributionally robust optimization R Kumar, K Majmundar, D Nagaraj, AS Suggala arXiv preprint arXiv:2306.09222, 2023 | 5 | 2023 |
