Rajiv Khanna

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 47

Top Topics

Subset Selection

Surface Geometry

Variational Inference

Rates Of Convergence

Top Venues

Publications

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)
Gregory Dexter, Borja Ocejo, S. Sathiya Keerthi, Aman Gupta, Ayan Acharya, Rajiv Khanna
A Precise Characterization of SGD Stability Using Loss Surface Geometry. CoRR (2024)
Andrea Agiollo, Young In Kim, Rajiv Khanna
Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization. KDD (2024)
Young In Kim, Pratiksha Agrawal, Johannes O. Royset, Rajiv Khanna
On Memorization and Privacy risks of Sharpness Aware Minimization. CoRR (2023)
Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. CoRR (2023)
Gregory Dexter, Rajiv Khanna, Jawad Raheel, Petros Drineas
Feature Space Sketching for Logistic Regression. CoRR (2023)
Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. COLT (2023)
Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney
Fast Feature Selection with Fairness Constraints. AISTATS (2023)
Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. ICML (2022)
Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney
Fast Feature Selection with Fairness Constraints. CoRR (2022)
Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification. ICLR (2021)
Jacky Zhang, Rajiv Khanna, Anastasios Kyrillidis, Sanmi Koyejo
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective. AISTATS (2021)
Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney
Geometric rates of convergence for kernel-based sampling algorithms. UAI (2021)
Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney
Generalization Properties of Stochastic Optimizers via Trajectory Analysis. CoRR (2021)
Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney
LocalNewton: Reducing Communication Bottleneck for Distributed Learning. CoRR (2021)
Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney
LocalNewton: Reducing communication rounds for distributed learning. UAI (2021)
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)
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 (2020)
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 (2020)
Michal Derezinski, Rajiv Khanna, Michael W. Mahoney
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method. NeurIPS (2020)
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)
Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney
Adversarially-Trained Deep Nets Transfer Better. CoRR (2020)
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo
Bayesian Coresets: An Optimization Perspective. CoRR (2020)
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo
Learning Sparse Distributions using Iterative Hard Thresholding. NeurIPS (2019)
Rajiv Khanna, Michael W. Mahoney
On Linear Convergence of Weighted Kernel Herding. CoRR (2019)
Rajiv Khanna, Been Kim, Joydeep Ghosh, Sanmi Koyejo
Interpreting Black Box Predictions using Fisher Kernels. AISTATS (2019)
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo
Learning Sparse Distributions using Iterative Hard Thresholding. CoRR (2019)
Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch
Boosting Black Box Variational Inference. NeurIPS (2018)
Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch
Boosting Black Box Variational Inference. CoRR (2018)
Shalmali Joshi, Rajiv Khanna, Joydeep Ghosh
Co-regularized Monotone Retargeting for Semi-supervised LeTOR. SDM (2018)
Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch
Boosting Variational Inference: an Optimization Perspective. AISTATS (2018)
Rajiv Khanna, Been Kim, Joydeep Ghosh, Oluwasanmi Koyejo
Interpreting Black Box Predictions using Fisher Kernels. CoRR (2018)
Rajiv Khanna, Anastasios Kyrillidis
IHT dies hard: Provable accelerated Iterative Hard Thresholding. AISTATS (2018)
Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh
Scalable Greedy Feature Selection via Weak Submodularity. AISTATS (2017)
Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. CoRR (2017)
Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. AISTATS (2017)
Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh
Scalable Greedy Feature Selection via Weak Submodularity. CoRR (2017)
Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo
Information Projection and Approximate Inference for Structured Sparse Variables. AISTATS (2017)
Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch
Boosting Variational Inference: an Optimization Perspective. CoRR (2017)
Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban
On Approximation Guarantees for Greedy Low Rank Optimization. CoRR (2017)
Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Joydeep Ghosh, Sahand N. Negahban
On Approximation Guarantees for Greedy Low Rank Optimization. ICML (2017)
Rajiv Khanna, Anastasios Kyrillidis
IHT dies hard: Provable accelerated Iterative Hard Thresholding. CoRR (2017)
Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo
A Deflation Method for Structured Probabilistic PCA. SDM (2017)
Rajiv Khanna, Michael Tschannen, Martin Jaggi
Pursuits in Structured Non-Convex Matrix Factorizations. CoRR (2016)
Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo
Information Projection and Approximate Inference for Structured Sparse Variables. CoRR (2016)
Been Kim, Oluwasanmi Koyejo, Rajiv Khanna
Examples are not enough, learn to criticize! Criticism for Interpretability. NIPS (2016)
Ethan R. Elenberg, Rajiv Khanna, Alexandros G. Dimakis, Sahand N. Negahban
Restricted Strong Convexity Implies Weak Submodularity. CoRR (2016)
Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo
Sparse Submodular Probabilistic PCA. AISTATS (2015)
S. Sathiya Keerthi, Tobias Schnabel, Rajiv Khanna
Towards a Better Understanding of Predict and Count Models. CoRR (2015)
Oluwasanmi Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack
On Prior Distributions and Approximate Inference for Structured Variables. NIPS (2014)
Rajiv Khanna, Liang Zhang, Deepak Agarwal, Bee-Chung Chen
Parallel matrix factorization for binary response. IEEE BigData (2013)
Rajiv Khanna, Liang Zhang, Deepak Agarwal, Bee-Chung Chen
Parallel Matrix Factorization for Binary Response CoRR (2012)
Deepak Agarwal, Rahul Agrawal, Rajiv Khanna, Nagaraj Kota
Estimating rates of rare events with multiple hierarchies through scalable log-linear models. KDD (2010)
Deepak Agarwal, Evgeniy Gabrilovich, Robert J. Hall, Vanja Josifovski, Rajiv Khanna
Translating relevance scores to probabilities for contextual advertising. CIKM (2009)
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chiru Bhattacharyya
Structured learning for non-smooth ranking losses. KDD (2008)
Weicheng Shen, Rajiv Khanna
Prolog to: Iris Recognition: An Emerging Biometric Technology. Proc. IEEE 85 (9) (1997)
Weicheng Shen, Rajiv Khanna
Prolog to: Fingerprint Features: Statistical Analysis And System Performance Estimates. Proc. IEEE 85 (9) (1997)
Weicheng Shen, Rajiv Khanna
Prolog to: An Identity-authentication System Using Fingerprints. Proc. IEEE 85 (9) (1997)
Weicheng Shen, Rajiv Khanna
Prolog to: Face Recognition: Eigenface, Elastic Matching, And Neural Nets. Proc. IEEE 85 (9) (1997)
Weicheng Shen, Rajiv Khanna
Prolog to: Speaker Recognition: A Tutorial. Proc. IEEE 85 (9) (1997)
Weicheng Shen, Rajiv Khanna
Prolog to: Evaluation Of Automated Biometrics-based Identification And Verification Systems. Proc. IEEE 85 (9) (1997)