Prolog to: Iris Recognition: An Emerging Biometric Technology. - Weicheng Shen; Rajiv Khanna (Proc. IEEE, 1997)

Prolog to: An Identity-authentication System Using Fingerprints. - Weicheng Shen; Rajiv Khanna (Proc. IEEE, 1997)

Prolog to: Fingerprint Features: Statistical Analysis And System Performance Estimates. - Weicheng Shen; Rajiv Khanna (Proc. IEEE, 1997)

Prolog to: Face Recognition: Eigenface, Elastic Matching, And Neural Nets. - Weicheng Shen; Rajiv Khanna (Proc. IEEE, 1997)

Prolog to: Speaker Recognition: A Tutorial. - Weicheng Shen; Rajiv Khanna (Proc. IEEE, 1997)

Prolog to: Evaluation Of Automated Biometrics-based Identification And Verification Systems. - Weicheng Shen; Rajiv Khanna (Proc. IEEE, 1997)

Structured learning for non-smooth ranking losses. - Soumen Chakrabarti; Rajiv Khanna; Uma Sawant; Chiru Bhattacharyya (KDD, 2008)

Translating relevance scores to probabilities for contextual advertising. - Deepak Agarwal; Evgeniy Gabrilovich; Robert J. Hall 002; Vanja Josifovski; Rajiv Khanna (CIKM, 2009)

Estimating rates of rare events with multiple hierarchies through scalable log-linear models. - Deepak Agarwal; Rahul Agrawal; Rajiv Khanna; Nagaraj Kota (KDD, 2010)

Parallel matrix factorization for binary response. - Rajiv Khanna; Liang Zhang 021; Deepak Agarwal; Bee-Chung Chen (IEEE BigData, 2013)

On Prior Distributions and Approximate Inference for Structured Variables. - Oluwasanmi Koyejo; Rajiv Khanna; Joydeep Ghosh; Russell A. Poldrack (NIPS, 2014)

Sparse Submodular Probabilistic PCA. - Rajiv Khanna; Joydeep Ghosh; Russell A. Poldrack; Oluwasanmi Koyejo (AISTATS, 2015)

Towards a Better Understanding of Predict and Count Models. - S. Sathiya Keerthi; Tobias Schnabel; Rajiv Khanna (CoRR, 2015)

Examples are not enough, learn to criticize! Criticism for Interpretability. - Been Kim; Oluwasanmi Koyejo; Rajiv Khanna (NIPS, 2016)

Restricted Strong Convexity Implies Weak Submodularity. - Ethan R. Elenberg; Rajiv Khanna; Alexandros G. Dimakis; Sahand N. Negahban (CoRR, 2016)

Pursuits in Structured Non-Convex Matrix Factorizations. - Rajiv Khanna; Michael Tschannen; Martin Jaggi (CoRR, 2016)

Scalable Greedy Feature Selection via Weak Submodularity. - Rajiv Khanna; Ethan R. Elenberg; Alexandros G. Dimakis; Sahand N. Negahban; Joydeep Ghosh (AISTATS, 2017)

Information Projection and Approximate Inference for Structured Sparse Variables. - Rajiv Khanna; Joydeep Ghosh; Russell A. Poldrack; Oluwasanmi Koyejo (AISTATS, 2017)

A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. - Francesco Locatello; Rajiv Khanna; Michael Tschannen; Martin Jaggi (AISTATS, 2017)

On Approximation Guarantees for Greedy Low Rank Optimization. - Rajiv Khanna; Ethan R. Elenberg; Alexandros G. Dimakis; Joydeep Ghosh; Sahand N. Negahban (ICML, 2017)

A Deflation Method for Structured Probabilistic PCA. - Rajiv Khanna; Joydeep Ghosh; Russell A. Poldrack; Oluwasanmi Koyejo (SDM, 2017)

On Approximation Guarantees for Greedy Low Rank Optimization. - Rajiv Khanna; Ethan R. Elenberg; Alexandros G. Dimakis; Sahand N. Negahban (CoRR, 2017)

IHT dies hard: Provable accelerated Iterative Hard Thresholding. - Rajiv Khanna; Anastasios Kyrillidis (AISTATS, 2018)

Boosting Variational Inference: an Optimization Perspective. - Francesco Locatello; Rajiv Khanna; Joydeep Ghosh; Gunnar Rätsch (AISTATS, 2018)

Boosting Black Box Variational Inference. - Francesco Locatello; Gideon Dresdner; Rajiv Khanna; Isabel Valera; Gunnar Rätsch (NeurIPS, 2018)

Co-regularized Monotone Retargeting for Semi-supervised LeTOR. - Shalmali Joshi; Rajiv Khanna; Joydeep Ghosh (SDM, 2018)

Interpreting Black Box Predictions using Fisher Kernels. - Rajiv Khanna; Been Kim; Joydeep Ghosh; Sanmi Koyejo (AISTATS, 2019)

Learning Sparse Distributions using Iterative Hard Thresholding. - Jacky Y. Zhang; Rajiv Khanna; Anastasios Kyrillidis; Oluwasanmi Koyejo (NeurIPS, 2019)

On Linear Convergence of Weighted Kernel Herding. - Rajiv Khanna; Michael W. Mahoney (CoRR, 2019)

Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method. - Michal Derezinski; Rajiv Khanna; Michael W. Mahoney (NeurIPS, 2020)

Boundary thickness and robustness in learning models. - Yaoqing Yang; Rajiv Khanna; Yaodong Yu; Amir Gholami; Kurt Keutzer; Joseph E. Gonzalez; Kannan Ramchandran; Michael W. Mahoney (NeurIPS, 2020)

Improved guarantees and a multiple-descent curve for the Column Subset Selection Problem and the Nyström method. - Michal Derezinski; Rajiv Khanna; Michael W. Mahoney (CoRR, 2020)

Bayesian Coresets: An Optimization Perspective. - Jacky Y. Zhang; Rajiv Khanna; Anastasios Kyrillidis; Oluwasanmi Koyejo (CoRR, 2020)

Adversarially-Trained Deep Nets Transfer Better. - Francisco Utrera; Evan Kravitz; N. Benjamin Erichson; Rajiv Khanna; Michael W. Mahoney (CoRR, 2020)

Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective. - Jacky Zhang; Rajiv Khanna; Anastasios Kyrillidis; Sanmi Koyejo (AISTATS, 2021)

Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification. - Francisco Utrera; Evan Kravitz; N. Benjamin Erichson; Rajiv Khanna; Michael W. Mahoney (ICLR, 2021)

Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract). - Michal Derezinski; Rajiv Khanna; Michael W. Mahoney (IJCAI, 2021)

LocalNewton: Reducing communication rounds for distributed learning. - Vipul Gupta; Avishek Ghosh; Michal Derezinski; Rajiv Khanna; Kannan Ramchandran; Michael W. Mahoney (UAI, 2021)

Geometric rates of convergence for kernel-based sampling algorithms. - Rajiv Khanna; Liam Hodgkinson; Michael W. Mahoney (UAI, 2021)

LocalNewton: Reducing Communication Bottleneck for Distributed Learning. - Vipul Gupta; Avishek Ghosh; Michal Derezinski; Rajiv Khanna; Kannan Ramchandran; Michael W. Mahoney (CoRR, 2021)

Generalization Properties of Stochastic Optimizers via Trajectory Analysis. - Liam Hodgkinson; Umut Simsekli; Rajiv Khanna; Michael W. Mahoney (CoRR, 2021)

Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. - Liam Hodgkinson; Umut Simsekli; Rajiv Khanna; Michael W. Mahoney (ICML, 2022)

Fast Feature Selection with Fairness Constraints. - Francesco Quinzan; Rajiv Khanna; Moshik Hershcovitch; Sarel Cohen; Daniel G. Waddington; Tobias Friedrich 001; Michael W. Mahoney (AISTATS, 2023)

Generalization Guarantees via Algorithm-dependent Rademacher Complexity. - Sarah Sachs; Tim van Erven; Liam Hodgkinson; Rajiv Khanna; Umut Simsekli (COLT, 2023)

Feature Space Sketching for Logistic Regression. - Gregory Dexter; Rajiv Khanna; Jawad Raheel; Petros Drineas (CoRR, 2023)

On Memorization and Privacy risks of Sharpness Aware Minimization. - Young In Kim; Pratiksha Agrawal; Johannes O. Royset; Rajiv Khanna (CoRR, 2023)

A Precise Characterization of SGD Stability Using Loss Surface Geometry. - Gregory Dexter; Borja Ocejo; S. Sathiya Keerthi; Aman Gupta; Ayan Acharya; Rajiv Khanna (ICLR, 2024)

Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization. - Andrea Agiollo; Young In Kim; Rajiv Khanna (KDD, 2024)

The Space Complexity of Approximating Logistic Loss. - Gregory Dexter; Petros Drineas; Rajiv Khanna (NeurIPS, 2024)

On the Support Vector Effect in DNNs: Rethinking Data Selection and Attribution. - Syed Hasan Amin Mahmood; Ming Yin 001; Rajiv Khanna (KDD (1), 2025)

Sharpness-Aware Machine Unlearning. - Haoran Tang; Rajiv Khanna (CoRR, 2025)