Rajiv Khanna
Rajiv Khanna
Postdoc, UC Berkeley
Verified email at berkeley.edu - Homepage
TitleCited byYear
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
1372016
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
1372016
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
1142008
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
952010
Scalable greedy feature selection via weak submodularity
R Khanna, E Elenberg, AG Dimakis, S Negahban, J Ghosh
arXiv preprint arXiv:1703.02723, 2017
362017
Restricted strong convexity implies weak submodularity
ER Elenberg, R Khanna, AG Dimakis, S Negahban
arXiv preprint arXiv:1612.00804, 2016
312016
A unified optimization view on generalized matching pursuit and frank-wolfe
F Locatello, R Khanna, M Tschannen, M Jaggi
arXiv preprint arXiv:1702.06457, 2017
302017
Restricted strong convexity implies weak submodularity
ER Elenberg, R Khanna, AG Dimakis, S Negahban
The Annals of Statistics 46 (6B), 3539-3568, 2018
262018
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
192009
Sparse submodular probabilistic PCA
R Khanna, J Ghosh, R Poldrack, O Koyejo
Artificial Intelligence and Statistics, 453-461, 2015
182015
On approximation guarantees for greedy low rank optimization
R Khanna, ER Elenberg, AG Dimakis, J Ghosh, S Negahban
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
162017
IHT dies hard: Provable accelerated iterative hard thresholding
R Khanna, A Kyrillidis
arXiv preprint arXiv:1712.09379, 2017
132017
Boosting variational inference: an optimization perspective
F Locatello, R Khanna, J Ghosh, G Rätsch
arXiv preprint arXiv:1708.01733, 2017
122017
On prior distributions and approximate inference for structured variables
OO Koyejo, R Khanna, J Ghosh, R Poldrack
Advances in Neural Information Processing Systems, 676-684, 2014
122014
Parallel matrix factorization for binary response
R Khanna, L Zhang, D Agarwal, BC Chen
2013 IEEE International Conference on Big Data, 430-438, 2013
112013
Interpreting black box predictions using fisher kernels
R Khanna, B Kim, J Ghosh, O Koyejo
arXiv preprint arXiv:1810.10118, 2018
102018
Boosting black box variational inference
F Locatello, G Dresdner, R Khanna, I Valera, G Rätsch
Advances in Neural Information Processing Systems, 3401-3411, 2018
102018
Towards a better understanding of predict and count models
SS Keerthi, T Schnabel, R Khanna
arXiv preprint arXiv:1511.02024, 2015
72015
A deflation method for structured probabilistic PCA
R Khanna, J Ghosh, R Poldrack, O Koyejo
Proceedings of the 2017 SIAM International Conference on Data Mining, 534-542, 2017
32017
Co-regularized Monotone Retargeting for Semi-supervised LeTOR
S Joshi, R Khanna, J Ghosh
Proceedings of the 2018 SIAM International Conference on Data Mining, 432-440, 2018
12018
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Articles 1–20