Rajiv Khanna - RankMe

73 papers A* 15A 14Journal 40Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration. AAAI Syed Hasan Amin Mahmood, Ming Yin, Rajiv Khanna
2026	J	`jnl`	Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration. CoRR Hasan Amin, Ming Yin, Rajiv Khanna
2026	J	`jnl`	From Logits to Latents: Contrastive Representation Shaping for LLM Unlearning. CoRR Haoran Tang, Rajiv Khanna
2026	J	`jnl`	Why Some Models Resist Unlearning: A Linear Stability Perspective. CoRR Wei-Kai Chang, Rajiv Khanna
2025	J	`jnl`	A Unified Stability Analysis of SAM vs SGD: Role of Data Coherence and Emergence of Simplicity Bias. CoRR Wei-Kai Chang, Rajiv Khanna
2025	—	`conf`	On the Support Vector Effect in DNNs: Rethinking Data Selection and Attribution. KDD (1) Syed Hasan Amin Mahmood, Ming Yin, Rajiv Khanna
2025	J	`jnl`	SAMOSA: Sharpness Aware Minimization for Open Set Active learning. CoRR Young In Kim, Andrea Agiollo, Rajiv Khanna
2025	J	`jnl`	Sharpness-Aware Machine Unlearning. CoRR Haoran Tang, Rajiv Khanna
2025	J	`jnl`	Stable Coresets via Posterior Sampling: Aligning Induced and Full Loss Landscapes. CoRR Wei-Kai Chang, Rajiv Khanna
2025	J	`jnl`	Structure-Aware Spectral Sparsification via Uniform Edge Sampling. CoRR Kaiwen He, Petros Drineas, Rajiv Khanna
2024	A*	`conf`	A Precise Characterization of SGD Stability Using Loss Surface Geometry. ICLR Gregory Dexter, Borja Ocejo, S. Sathiya Keerthi, Aman Gupta, Ayan Acharya, Rajiv Khanna
2024	J	`jnl`	A Precise Characterization of SGD Stability Using Loss Surface Geometry. CoRR Gregory Dexter, Borja Ocejo, S. Sathiya Keerthi, Aman Gupta, Ayan Acharya, Rajiv Khanna
2024	A*	`conf`	Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization. KDD Andrea Agiollo, Young In Kim, Rajiv Khanna
2024	A*	`conf`	The Space Complexity of Approximating Logistic Loss. NeurIPS Gregory Dexter, Petros Drineas, Rajiv Khanna
2024	J	`jnl`	The Space Complexity of Approximating Logistic Loss. CoRR Gregory Dexter, Petros Drineas, Rajiv Khanna
2023	A	`conf`	Fast Feature Selection with Fairness Constraints. AISTATS Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney
2023	J	`jnl`	Feature Space Sketching for Logistic Regression. CoRR Gregory Dexter, Rajiv Khanna, Jawad Raheel, Petros Drineas
2023	A*	`conf`	Generalization Guarantees via Algorithm-dependent Rademacher Complexity. COLT Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli
2023	J	`jnl`	Generalization Guarantees via Algorithm-dependent Rademacher Complexity. CoRR Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli
2023	J	`jnl`	On Memorization and Privacy risks of Sharpness Aware Minimization. CoRR Young In Kim, Pratiksha Agrawal, Johannes O. Royset, Rajiv Khanna
2022	J	`jnl`	Fast Feature Selection with Fairness Constraints. CoRR Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney
2022	A*	`conf`	Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. ICML Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney
2021	A*	`conf`	Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification. ICLR Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney
2021	A	`conf`	Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective. AISTATS Jacky Zhang, Rajiv Khanna, Anastasios Kyrillidis, Sanmi Koyejo
2021	J	`jnl`	Generalization Properties of Stochastic Optimizers via Trajectory Analysis. CoRR Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney
2021	A	`conf`	Geometric rates of convergence for kernel-based sampling algorithms. UAI Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney
2021	A*	`conf`	Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract). IJCAI Michal Derezinski, Rajiv Khanna, Michael W. Mahoney
2021	J	`jnl`	LocalNewton: Reducing Communication Bottleneck for Distributed Learning. CoRR Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney
2021	A	`conf`	LocalNewton: Reducing communication rounds for distributed learning. UAI Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney
2020	J	`jnl`	Adversarially-Trained Deep Nets Transfer Better. CoRR Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney
2020	J	`jnl`	Bayesian Coresets: An Optimization Perspective. CoRR Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo
2020	A*	`conf`	Boundary thickness and robustness in learning models. NeurIPS Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney
2020	J	`jnl`	Boundary thickness and robustness in learning models. CoRR Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney
2020	A*	`conf`	Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method. NeurIPS Michal Derezinski, Rajiv Khanna, Michael W. Mahoney
2020	J	`jnl`	Improved guarantees and a multiple-descent curve for the Column Subset Selection Problem and the Nyström method. CoRR Michal Derezinski, Rajiv Khanna, Michael W. Mahoney
2019	A	`conf`	Interpreting Black Box Predictions using Fisher Kernels. AISTATS Rajiv Khanna, Been Kim, Joydeep Ghosh, Sanmi Koyejo
2019	A*	`conf`	Learning Sparse Distributions using Iterative Hard Thresholding. NeurIPS Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo
2019	J	`jnl`	Learning Sparse Distributions using Iterative Hard Thresholding. CoRR Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo
2019	J	`jnl`	On Linear Convergence of Weighted Kernel Herding. CoRR Rajiv Khanna, Michael W. Mahoney
2018	A*	`conf`	Boosting Black Box Variational Inference. NeurIPS Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch
2018	J	`jnl`	Boosting Black Box Variational Inference. CoRR Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch
2018	A	`conf`	Boosting Variational Inference: an Optimization Perspective. AISTATS Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch
2018	A	`conf`	Co-regularized Monotone Retargeting for Semi-supervised LeTOR. SDM Shalmali Joshi, Rajiv Khanna, Joydeep Ghosh
2018	A	`conf`	IHT dies hard: Provable accelerated Iterative Hard Thresholding. AISTATS Rajiv Khanna, Anastasios Kyrillidis
2018	J	`jnl`	Interpreting Black Box Predictions using Fisher Kernels. CoRR Rajiv Khanna, Been Kim, Joydeep Ghosh, Oluwasanmi Koyejo
2017	A	`conf`	A Deflation Method for Structured Probabilistic PCA. SDM Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo
2017	A	`conf`	A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. AISTATS Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi
2017	J	`jnl`	A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. CoRR Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi
2017	J	`jnl`	Boosting Variational Inference: an Optimization Perspective. CoRR Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch
2017	J	`jnl`	IHT dies hard: Provable accelerated Iterative Hard Thresholding. CoRR Rajiv Khanna, Anastasios Kyrillidis
2017	A	`conf`	Information Projection and Approximate Inference for Structured Sparse Variables. AISTATS Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo
2017	A*	`conf`	On Approximation Guarantees for Greedy Low Rank Optimization. ICML Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Joydeep Ghosh, Sahand N. Negahban
2017	J	`jnl`	On Approximation Guarantees for Greedy Low Rank Optimization. CoRR Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban
2017	A	`conf`	Scalable Greedy Feature Selection via Weak Submodularity. AISTATS Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh
2017	J	`jnl`	Scalable Greedy Feature Selection via Weak Submodularity. CoRR Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Sahand N. Negahban, Joydeep Ghosh
2016	—	`conf`	Examples are not enough, learn to criticize! Criticism for Interpretability. NIPS Been Kim, Oluwasanmi Koyejo, Rajiv Khanna
2016	J	`jnl`	Information Projection and Approximate Inference for Structured Sparse Variables. CoRR Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo
2016	J	`jnl`	Pursuits in Structured Non-Convex Matrix Factorizations. CoRR Rajiv Khanna, Michael Tschannen, Martin Jaggi
2016	J	`jnl`	Restricted Strong Convexity Implies Weak Submodularity. CoRR Ethan R. Elenberg, Rajiv Khanna, Alexandros G. Dimakis, Sahand N. Negahban
2015	A	`conf`	Sparse Submodular Probabilistic PCA. AISTATS Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo
2015	J	`jnl`	Towards a Better Understanding of Predict and Count Models. CoRR S. Sathiya Keerthi, Tobias Schnabel, Rajiv Khanna
2014	—	`conf`	On Prior Distributions and Approximate Inference for Structured Variables. NIPS Oluwasanmi Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack
2013	—	`conf`	Parallel matrix factorization for binary response. IEEE BigData Rajiv Khanna, Liang Zhang, Deepak Agarwal, Bee-Chung Chen
2012	J	`jnl`	Parallel Matrix Factorization for Binary Response CoRR Rajiv Khanna, Liang Zhang, Deepak Agarwal, Bee-Chung Chen
2010	A*	`conf`	Estimating rates of rare events with multiple hierarchies through scalable log-linear models. KDD Deepak Agarwal, Rahul Agrawal, Rajiv Khanna, Nagaraj Kota
2009	A	`conf`	Translating relevance scores to probabilities for contextual advertising. CIKM Deepak Agarwal, Evgeniy Gabrilovich, Robert J. Hall, Vanja Josifovski, Rajiv Khanna
2008	A*	`conf`	Structured learning for non-smooth ranking losses. KDD Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chiru Bhattacharyya
1997	J	`jnl`	Prolog to: An Identity-authentication System Using Fingerprints. Proc. IEEE Weicheng Shen, Rajiv Khanna
1997	J	`jnl`	Prolog to: Evaluation Of Automated Biometrics-based Identification And Verification Systems. Proc. IEEE Weicheng Shen, Rajiv Khanna
1997	J	`jnl`	Prolog to: Face Recognition: Eigenface, Elastic Matching, And Neural Nets. Proc. IEEE Weicheng Shen, Rajiv Khanna
1997	J	`jnl`	Prolog to: Fingerprint Features: Statistical Analysis And System Performance Estimates. Proc. IEEE Weicheng Shen, Rajiv Khanna
1997	J	`jnl`	Prolog to: Iris Recognition: An Emerging Biometric Technology. Proc. IEEE Weicheng Shen, Rajiv Khanna
1997	J	`jnl`	Prolog to: Speaker Recognition: A Tutorial. Proc. IEEE Weicheng Shen, Rajiv Khanna