| 2026 | AAAI | Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration. | Syed Hasan Amin Mahmood, Ming Yin, Rajiv Khanna |
| 2026 | ACL | From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives? | Hasan Amin, Harry Yizhou Tian, Xiaoni Duan, Chien-Ju Ho, Rajiv Khanna, Ming Yin |
| 2026 | COLT | Spectral Valleys and Sharp Failures in Greedy Determinant Maximization. | Rajiv Khanna |
| 2025 | KDD | On the Support Vector Effect in DNNs: Rethinking Data Selection and Attribution. | Syed Hasan Amin Mahmood, Ming Yin, Rajiv Khanna |
| 2024 | ICLR | A Precise Characterization of SGD Stability Using Loss Surface Geometry. | Gregory Dexter, Borja Ocejo, S. Sathiya Keerthi, Aman Gupta, Ayan Acharya, Rajiv Khanna |
| 2024 | KDD | Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization. | Andrea Agiollo, Young In Kim, Rajiv Khanna |
| 2023 | AISTATS | Fast Feature Selection with Fairness Constraints. | Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney |
| 2023 | COLT | Generalization Guarantees via Algorithm-dependent Rademacher Complexity. | Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli |
| 2022 | ICML | Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. | Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney |
| 2021 | AISTATS | Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective. | Jacky Zhang, Rajiv Khanna, Anastasios Kyrillidis, Sanmi Koyejo |
| 2021 | ICLR | Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification. | Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney |
| 2021 | IJCAI | Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract). | Michal Derezinski, Rajiv Khanna, Michael W. Mahoney |
| 2021 | UAI | LocalNewton: Reducing communication rounds for distributed learning. | Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney |
| 2021 | UAI | Geometric rates of convergence for kernel-based sampling algorithms. | Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney |
| 2019 | AISTATS | Interpreting Black Box Predictions using Fisher Kernels. | Rajiv Khanna, Been Kim, Joydeep Ghosh, Sanmi Koyejo |
| 2018 | AISTATS | IHT dies hard: Provable accelerated Iterative Hard Thresholding. | Rajiv Khanna, Anastasios Kyrillidis |
| 2018 | AISTATS | Boosting Variational Inference: an Optimization Perspective. | Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rtsch |
| 2018 | SDM | Co-regularized Monotone Retargeting for Semi-supervised LeTOR. | Shalmali Joshi, Rajiv Khanna, Joydeep Ghosh |
| 2017 | AISTATS | Scalable Greedy Feature Selection via Weak Submodularity. | Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh |
| 2017 | AISTATS | Information Projection and Approximate Inference for Structured Sparse Variables. | Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo |
| 2017 | AISTATS | A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. | Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi |
| 2017 | ICML | On Approximation Guarantees for Greedy Low Rank Optimization. | Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Joydeep Ghosh, Sahand N. Negahban |
| 2017 | SDM | A Deflation Method for Structured Probabilistic PCA. | Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo |
| 2015 | AISTATS | Sparse Submodular Probabilistic PCA. | Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo |
| 2010 | KDD | Estimating rates of rare events with multiple hierarchies through scalable log-linear models. | Deepak Agarwal, Rahul Agrawal, Rajiv Khanna, Nagaraj Kota |
| 2009 | CIKM | Translating relevance scores to probabilities for contextual advertising. | Deepak Agarwal, Evgeniy Gabrilovich, Robert J. Hall, Vanja Josifovski, Rajiv Khanna |
| 2008 | KDD | Structured learning for non-smooth ranking losses. | Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chiru Bhattacharyya |