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Rajiv Khanna
Rajiv Khanna
Assistant Prof, PurdueCS
在 purdue.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
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
9932016
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
9932016
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
1492008
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
1192010
Restricted strong convexity implies weak submodularity
ER Elenberg, R Khanna, AG Dimakis, S Negahban
The Annals of Statistics 46 (6B), 3539-3568, 2018
1162018
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
1022019
Scalable greedy feature selection via weak submodularity
R Khanna, E Elenberg, A Dimakis, S Negahban, J Ghosh
Artificial Intelligence and Statistics, 1560-1568, 2017
932017
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
762020
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
622017
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
452020
Restricted strong convexity implies weak submodularity
ER Elenberg, R Khanna, AG Dimakis, S Negahban
arXiv preprint arXiv:1612.00804, 2016
442016
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
392020
IHT dies hard: Provable accelerated iterative hard thresholding
R Khanna, A Kyrillidis
International Conference on Artificial Intelligence and Statistics, 188-198, 2018
372018
Boosting variational inference: an optimization perspective
F Locatello, R Khanna, J Ghosh, G Ratsch
International Conference on Artificial Intelligence and Statistics, 464-472, 2018
372018
Boosting black box variational inference
F Locatello, G Dresdner, R Khanna, I Valera, G Rätsch
Advances in Neural Information Processing Systems 31, 2018
292018
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
262021
Sparse submodular probabilistic PCA
R Khanna, J Ghosh, R Poldrack, O Koyejo
Artificial Intelligence and Statistics, 453-461, 2015
232015
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
222017
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
202009
Trichophagia along with trichobezoar in the absence of trichotillomania
A Mehra, A Avasthi, V Gupta, S Grover
Journal of Neurosciences in Rural Practice 5 (S 01), S055-S057, 2014
162014
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