Rajiv Khanna
Email: [first name]ak@purdue.edu
Google Scholar
DBLP CV

Publications

  • Geometric Rates of Convergence for Kernel-based Sampling Algorithms Rajiv Khanna, Liam Hodgkinson, Michael Mahoney UAI 2021
  • LocalNewton: Reducing Communication Bottleneck for Distributed Learning Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Ramchandran Kannan, Michael Mahoney UAI 2021
  • Bayesian Coresets: An Optimization Perspective Yibo Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo. AISTATS 2021 (Oral)
  • Adversarially-trained deep nets transfer better 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 Michal Derezinski, Rajiv Khanna, Michael W Mahoney Neurips 2020 (Best paper award. Top 3 papers out of over 9400 submissions)
  • 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
  • Learning Sparse Distributions using Iterative Hard Thresholding Yibo Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo. Neurips 2019
  • Interpreting black box predictions using fisher kernels Rajiv Khanna, Been Kim, Joydeep Ghosh, Oluwasanmi Koyejo. AISTATS 2019
  • Boosting Black Box Variational Inference Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar R├Ątsch. NIPS 2018 (Spotlight)
  • Restricted Strong Convexity implies Weak Submodularity Ethan Elenberg, Rajiv Khanna, Alex Dimakis, Sahand Neghaban. Accepted to Annals of Stat. 2018 (A shorter version presented at NIPS 2016 Workshop on Learning in High Dimensions with Structure.) [paper][bibtex]
  • Provable Accelerated Iterative Hard Thresholding. Rajiv Khanna, Anastasios Kyrillidis. AISTATS 2018
  • Boosting Variational Inference: An Optimization Perspective Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Raetsch ( Preprint )AISTATS 2018 (a shorter version appeared in NIPS 2017 workshop on Approx. Variational Inference)
  • Co-regularized Monotone Retargeting for Semi-supervised LeTOR Shalmali Joshi, Rajiv Khanna, Joydeep Ghosh SDM 2018
  • On Approximation Guarantees for Greedy Low Rank Optimization Rajiv Khanna, Ethan Elenberg, Alex Dimakis, Joydeep Ghosh, Sahand Neghaban. ICML 2017 [paper] [bibtex]
  • Scalable Greedy Feature Selection via Weak Submodularity. Rajiv Khanna, Ethan Elenberg, Alex Dimakis, Sahand Neghaban, Joydeep Ghosh. AISTATS 2017 [paper] [bibtex]
  • Information Projection and Approximate Inference for Structured Sparse Variables. Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo. AISTATS 2017 [paper] [bibtex]
  • A Unified Analysis of Frank Wolfe and Matching Pursuit. Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi AISTATS 2017 [paper] [bibtex]
  • A Deflation Method for Structured Probabilistic PCA. Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo. SDM 2017 [paper] [bibtex]
  • Examples are not Enough, Learn to Criticize! Criticism for Interpretability Been Kim*, Rajiv Khanna*, Oluwasanmi Koyejo* NIPS 2016 (Oral) [paper] [Talk slides] [code] [bibtex]
  • Pursuits in Structured Non-Convex Matrix Factorizations Rajiv Khanna, Francesco Locatello, Michael Tschannen, Martin Jaggi. [paper][bibtex]
  • Towards a Better Understanding of Predict and Count Models. S. Sathiya Keerthi, Tobias Schnabel, Rajiv Khanna. [paper]
  • Sparse Submodular Probabilistic PCA Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi O. Koyejo AISTATS 2015 (Oral) [paper][bibtex]
  • A Deflation Method for Probabilistic PCA. Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference [paper]
  • On Prior Distributions and Approximate Inference for Structured Variables Oluwasanmi O. Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack NIPS 2014 [paper] [ supplement ] [bibtex]
  • DPM: A State Space Model for Large-Scale Direct Marketing Yubin Park, Rajiv Khanna, Joydeep Ghosh, Daniel Mihalko. CoRR abs/1507.01135
  • Parallel matrix factorization for binary response. Rajiv Khanna, Liang Zhang, Deepak Agarwal, Bee-Chung Chen. IEEE BigData Conference 2013: 430-438
  • Estimating rates of rare events with multiple hierarchies through scalable log-linear models. Deepak Agarwal, Rahul Agrawal, Rajiv Khanna, Nagaraj Kota. KDD 2010: 213-222
  • Translating relevance scores to probabilities for contextual advertising. Deepak Agarwal, Evgeniy Gabrilovich, Robert Hall, Vanja Josifovski, Rajiv Khanna CIKM 2009: 1899-1902
  • Structured learning for non-smooth ranking losses. Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chiru Bhattacharyya. KDD 2008: 88-96