Khanna, Rajiv

28 publications

NeurIPS 2025 A Unified Stability Analysis of SAM vs SGD: Role of Data Coherence and Emergence of Simplicity Bias Wei-Kai Chang, Rajiv Khanna

NeurIPS 2025 Stable Coresets via Posterior Sampling: Aligning Induced and Full Loss Landscapes Wei-Kai Chang, Rajiv Khanna

NeurIPS 2025 Structure-Aware Spectral Sparsification via Uniform Edge Sampling Kaiwen He, Petros Drineas, Rajiv Khanna

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

NeurIPS 2024 The Space Complexity of Approximating Logistic Loss Gregory Dexter, Petros Drineas, Rajiv Khanna

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

COLT 2023 Generalization Guarantees via Algorithm-Dependent Rademacher Complexity Sarah Sachs, Tim Erven, Liam Hodgkinson, Rajiv Khanna, Umut Şimşekli

ICML 2022 Generalization Bounds Using Lower Tail Exponents in Stochastic Optimizers Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael Mahoney

AISTATS 2021 Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective Jacky Zhang, Rajiv Khanna, Anastasios Kyrillidis, Sanmi Koyejo

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

NeurIPSW 2021 Distribution Preserving Bayesian Coresets Using Set Constraints Shovik Guha, Rajiv Khanna, Oluwasanmi O Koyejo

UAI 2021 Geometric Rates of Convergence for Kernel-Based Sampling Algorithms Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney

IJCAI 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

UAI 2021 LocalNewton: Reducing Communication Rounds for Distributed Learning Vipul Gupta, Avishek Ghosh, Michał Dereziński, Rajiv Khanna, Kannan Ramchandran, 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 Column Subset Selection and the Nystrom Method Michal Derezinski, Rajiv Khanna, Michael W. Mahoney

AISTATS 2019 Interpreting Black Box Predictions Using Fisher Kernels Rajiv Khanna, Been Kim, Joydeep Ghosh, Sanmi Koyejo

NeurIPS 2019 Learning Sparse Distributions Using Iterative Hard Thresholding Jacky Y Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi O Koyejo

NeurIPS 2018 Boosting Black Box Variational Inference Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Raetsch

AISTATS 2018 Boosting Variational Inference: An Optimization Perspective Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch

AISTATS 2018 IHT Dies Hard: Provable Accelerated Iterative Hard Thresholding Rajiv Khanna, Anastasios Kyrillidis

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

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

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

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

NeurIPS 2016 Examples Are Not Enough, Learn to Criticize! Criticism for Interpretability Been Kim, Rajiv Khanna, Oluwasanmi O Koyejo

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

NeurIPS 2014 On Prior Distributions and Approximate Inference for Structured Variables Oluwasanmi O Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell Poldrack