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ORCIDRajiv Khanna
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2020 – today
- 2024
[c30]Gregory Dexter, Borja Ocejo, S. Sathiya Keerthi, Aman Gupta, Ayan Acharya, Rajiv Khanna:
A Precise Characterization of SGD Stability Using Loss Surface Geometry. ICLR 2024- [c29]Andrea Agiollo
, Young In Kim, Rajiv Khanna:
Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization. KDD 2024: 17-28 - [i25]Gregory Dexter, Borja Ocejo, S. Sathiya Keerthi, Aman Gupta, Ayan Acharya, Rajiv Khanna:
A Precise Characterization of SGD Stability Using Loss Surface Geometry. CoRR abs/2401.12332 (2024) - 2023
- [c28]Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney:
Fast Feature Selection with Fairness Constraints. AISTATS 2023: 7800-7823 - [c27]Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli:
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. COLT 2023: 4863-4880 - [i24]Gregory Dexter, Rajiv Khanna, Jawad Raheel, Petros Drineas:
Feature Space Sketching for Logistic Regression. CoRR abs/2303.14284 (2023) - [i23]Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli:
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. CoRR abs/2307.02501 (2023) - [i22]Young In Kim, Pratiksha Agrawal, Johannes O. Royset, Rajiv Khanna:
On Memorization and Privacy risks of Sharpness Aware Minimization. CoRR abs/2310.00488 (2023) - 2022
- [c26]Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney:
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. ICML 2022: 8774-8795 - [i21]Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney:
Fast Feature Selection with Fairness Constraints. CoRR abs/2202.13718 (2022) - 2021
- [c25]Jacky Zhang, Rajiv Khanna, Anastasios Kyrillidis, Sanmi Koyejo:
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective. AISTATS 2021: 2782-2790 - [c24]Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney:
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification. ICLR 2021 - [c23]Michal Derezinski, Rajiv Khanna, Michael W. Mahoney:
Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract). IJCAI 2021: 4765-4769 - [c22]Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney:
LocalNewton: Reducing communication rounds for distributed learning. UAI 2021: 632-642 - [c21]Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney:
Geometric rates of convergence for kernel-based sampling algorithms. UAI 2021: 2156-2164 - [i20]Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney:
LocalNewton: Reducing Communication Bottleneck for Distributed Learning. CoRR abs/2105.07320 (2021) - [i19]Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney:
Generalization Properties of Stochastic Optimizers via Trajectory Analysis. CoRR abs/2108.00781 (2021) - 2020
- [c20]Michal Derezinski, Rajiv Khanna, Michael W. Mahoney:
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method. NeurIPS 2020 - [c19]Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Boundary thickness and robustness in learning models. NeurIPS 2020 - [i18]Michal Derezinski, Rajiv Khanna, Michael W. Mahoney:
Improved guarantees and a multiple-descent curve for the Column Subset Selection Problem and the Nyström method. CoRR abs/2002.09073 (2020) - [i17]Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo:
Bayesian Coresets: An Optimization Perspective. CoRR abs/2007.00715 (2020) - [i16]Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Boundary thickness and robustness in learning models. CoRR abs/2007.05086 (2020) - [i15]Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney:
Adversarially-Trained Deep Nets Transfer Better. CoRR abs/2007.05869 (2020)
2010 – 2019
- 2019
- [c18]Rajiv Khanna, Been Kim, Joydeep Ghosh, Sanmi Koyejo:
Interpreting Black Box Predictions using Fisher Kernels. AISTATS 2019: 3382-3390 - [c17]Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo:
Learning Sparse Distributions using Iterative Hard Thresholding. NeurIPS 2019: 6757-6766 - [i14]Rajiv Khanna, Michael W. Mahoney:
On Linear Convergence of Weighted Kernel Herding. CoRR abs/1907.08410 (2019) - [i13]Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo:
Learning Sparse Distributions using Iterative Hard Thresholding. CoRR abs/1910.13389 (2019) - 2018
- [c16]Rajiv Khanna, Anastasios Kyrillidis:
IHT dies hard: Provable accelerated Iterative Hard Thresholding. AISTATS 2018: 188-198 - [c15]Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch:
Boosting Variational Inference: an Optimization Perspective. AISTATS 2018: 464-472 - [c14]Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch:
Boosting Black Box Variational Inference. NeurIPS 2018: 3405-3415 - [c13]Shalmali Joshi, Rajiv Khanna, Joydeep Ghosh:
Co-regularized Monotone Retargeting for Semi-supervised LeTOR. SDM 2018: 432-440 - [i12]Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch:
Boosting Black Box Variational Inference. CoRR abs/1806.02185 (2018) - [i11]Rajiv Khanna, Been Kim, Joydeep Ghosh, Oluwasanmi Koyejo:
Interpreting Black Box Predictions using Fisher Kernels. CoRR abs/1810.10118 (2018) - 2017
- [c12]Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi:
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. AISTATS 2017: 860-868 - [c11]Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo:
Information Projection and Approximate Inference for Structured Sparse Variables. AISTATS 2017: 1358-1366 - [c10]Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh:
Scalable Greedy Feature Selection via Weak Submodularity. AISTATS 2017: 1560-1568 - [c9]Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Joydeep Ghosh, Sahand N. Negahban:
On Approximation Guarantees for Greedy Low Rank Optimization. ICML 2017: 1837-1846 - [c8]Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo:
A Deflation Method for Structured Probabilistic PCA. SDM 2017: 534-542 - [i10]Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi:
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. CoRR abs/1702.06457 (2017) - [i9]Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban:
On Approximation Guarantees for Greedy Low Rank Optimization. CoRR abs/1703.02721 (2017) - [i8]Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh:
Scalable Greedy Feature Selection via Weak Submodularity. CoRR abs/1703.02723 (2017) - [i7]Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch:
Boosting Variational Inference: an Optimization Perspective. CoRR abs/1708.01733 (2017) - [i6]Rajiv Khanna, Anastasios Kyrillidis:
IHT dies hard: Provable accelerated Iterative Hard Thresholding. CoRR abs/1712.09379 (2017) - 2016
- [c7]Been Kim, Oluwasanmi Koyejo, Rajiv Khanna:
Examples are not enough, learn to criticize! Criticism for Interpretability. NIPS 2016: 2280-2288 - [i5]Rajiv Khanna, Michael Tschannen, Martin Jaggi:
Pursuits in Structured Non-Convex Matrix Factorizations. CoRR abs/1602.04208 (2016) - [i4]Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo:
Information Projection and Approximate Inference for Structured Sparse Variables. CoRR abs/1607.03204 (2016) - [i3]Ethan R. Elenberg, Rajiv Khanna, Alexandros G. Dimakis, Sahand N. Negahban:
Restricted Strong Convexity Implies Weak Submodularity. CoRR abs/1612.00804 (2016) - 2015
- [c6]Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo:
Sparse Submodular Probabilistic PCA. AISTATS 2015 - [i2]S. Sathiya Keerthi, Tobias Schnabel, Rajiv Khanna:
Towards a Better Understanding of Predict and Count Models. CoRR abs/1511.02024 (2015) - 2014
- [c5]Oluwasanmi Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack:
On Prior Distributions and Approximate Inference for Structured Variables. NIPS 2014: 676-684 - 2013
- [c4]Rajiv Khanna, Liang Zhang, Deepak Agarwal, Bee-Chung Chen:
Parallel matrix factorization for binary response. IEEE BigData 2013: 430-438 - 2012
- [i1]Rajiv Khanna, Liang Zhang, Deepak Agarwal, Bee-Chung Chen:
Parallel Matrix Factorization for Binary Response. CoRR abs/1203.5124 (2012) - 2010
- [c3]Deepak Agarwal, Rahul Agrawal, Rajiv Khanna, Nagaraj Kota:
Estimating rates of rare events with multiple hierarchies through scalable log-linear models. KDD 2010: 213-222
2000 – 2009
- 2009
- [c2]Deepak Agarwal, Evgeniy Gabrilovich, Robert J. Hall, Vanja Josifovski, Rajiv Khanna:
Translating relevance scores to probabilities for contextual advertising. CIKM 2009: 1899-1902 - 2008
- [c1]Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chiru Bhattacharyya:
Structured learning for non-smooth ranking losses. KDD 2008: 88-96
1990 – 1999
- 1997
- [j6]Weicheng Shen, Rajiv Khanna:
Prolog to: Iris Recognition: An Emerging Biometric Technology. Proc. IEEE 85(9): 1347 (1997) - [j5]Weicheng Shen, Rajiv Khanna:
Prolog to: An Identity-authentication System Using Fingerprints. Proc. IEEE 85(9): 1364 (1997) - [j4]Weicheng Shen, Rajiv Khanna:
Prolog to: Fingerprint Features: Statistical Analysis And System Performance Estimates. Proc. IEEE 85(9): 1389 (1997) - [j3]Weicheng Shen, Rajiv Khanna:
Prolog to: Face Recognition: Eigenface, Elastic Matching, And Neural Nets. Proc. IEEE 85(9): 1422 (1997) - [j2]Weicheng Shen, Rajiv Khanna:
Prolog to: Speaker Recognition: A Tutorial. Proc. IEEE 85(9): 1436 (1997) - [j1]Weicheng Shen, Rajiv Khanna:
Prolog to: Evaluation Of Automated Biometrics-based Identification And Verification Systems. Proc. IEEE 85(9): 1463 (1997)
Coauthor Index
aka: Sanmi Koyejo
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last updated on 2024-09-10 01:17 CEST by the dblp team
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