| Examples are not Enough, Learn to Criticize! Criticism for Interpretability B Kim, R Khanna, O Koyejo Advances in Neural Information Processing Systems 29 (NIPS 2016) 29, 2280--2288, 2016 | 1095 | 2016 |
| Examples are not enough, learn to criticize! criticism for interpretability B Kim, R Khanna, OO Koyejo Advances in Neural Information Processing Systems, 2280-2288, 2016 | 1095 | 2016 |
| Structured learning for non-smooth ranking losses S Chakrabarti, R Khanna, U Sawant, C Bhattacharyya Proceedings of the 14th ACM SIGKDD international conference on knowledge …, 2008 | 155 | 2008 |
| Restricted strong convexity implies weak submodularity ER Elenberg, R Khanna, AG Dimakis, S Negahban The Annals of Statistics 46 (6B), 3539-3568, 2018 | 122 | 2018 |
| Estimating rates of rare events with multiple hierarchies through scalable log-linear models D Agarwal, R Agrawal, R Khanna, N Kota Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010 | 122 | 2010 |
| Interpreting black box predictions using fisher kernels R Khanna, B Kim, J Ghosh, S Koyejo The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 113 | 2019 |
| Scalable greedy feature selection via weak submodularity R Khanna, E Elenberg, A Dimakis, S Negahban, J Ghosh Artificial Intelligence and Statistics, 1560-1568, 2017 | 99 | 2017 |
| Adversarially-trained deep nets transfer better: Illustration on image classification F Utrera, E Kravitz, NB Erichson, R Khanna, MW Mahoney arXiv preprint arXiv:2007.05869, 2020 | 86 | 2020 |
| A unified optimization view on generalized matching pursuit and frank-wolfe F Locatello, R Khanna, M Tschannen, M Jaggi Artificial Intelligence and Statistics, 860-868, 2017 | 64 | 2017 |
| Improved guarantees and a multiple-descent curve for column subset selection and the nystrom method M Derezinski, R Khanna, MW Mahoney Advances in Neural Information Processing Systems 33, 4953-4964, 2020 | 49 | 2020 |
| Restricted strong convexity implies weak submodularity ER Elenberg, R Khanna, AG Dimakis, S Negahban arXiv preprint arXiv:1612.00804, 2016 | 46 | 2016 |
| Boundary thickness and robustness in learning models Y Yang, R Khanna, Y Yu, A Gholami, K Keutzer, JE Gonzalez, ... Advances in Neural Information Processing Systems 33, 6223-6234, 2020 | 45 | 2020 |
| IHT dies hard: Provable accelerated iterative hard thresholding R Khanna, A Kyrillidis International Conference on Artificial Intelligence and Statistics, 188-198, 2018 | 38 | 2018 |
| Boosting variational inference: an optimization perspective F Locatello, R Khanna, J Ghosh, G Ratsch International Conference on Artificial Intelligence and Statistics, 464-472, 2018 | 38 | 2018 |
| Boosting black box variational inference F Locatello, G Dresdner, R Khanna, I Valera, G Rätsch Advances in Neural Information Processing Systems 31, 2018 | 35 | 2018 |
| Bayesian coresets: Revisiting the nonconvex optimization perspective J Zhang, R Khanna, A Kyrillidis, S Koyejo International Conference on Artificial Intelligence and Statistics, 2782-2790, 2021 | 27 | 2021 |
| Sparse submodular probabilistic PCA R Khanna, J Ghosh, R Poldrack, O Koyejo Artificial Intelligence and Statistics, 453-461, 2015 | 23 | 2015 |
| On approximation guarantees for greedy low rank optimization R Khanna, ER Elenberg, AG Dimakis, J Ghosh, S Negahban International Conference on Machine Learning, 1837-1846, 2017 | 22 | 2017 |
| Translating relevance scores to probabilities for contextual advertising D Agarwal, E Gabrilovich, R Hall, V Josifovski, R Khanna Proceedings of the 18th ACM conference on Information and knowledge …, 2009 | 20 | 2009 |
| Generalization bounds using lower tail exponents in stochastic optimizers L Hodgkinson, U Simsekli, R Khanna, M Mahoney International Conference on Machine Learning, 8774-8795, 2022 | 18 | 2022 |
