Bayesian Coresets
Rajiv Khanna
Publications
Curvature-Aware Active Statistical Inference : Reducing Labeling via Data Coherence
Pinaki Mohanty, Rajiv Khanna
Adaptive Stratified Active Statistical Inference
Pinaki Mohanty, Rajiv Khanna
Spectral Valleys and Sharp Failures in Greedy Determinant Maximization
Rajiv Khanna
Consistent Diffusion Language Models
Syed Hasan Amin Mahmood, Yuan Gao, Yaser Souri, Subhojit Som, Ming Yin, Rajiv Khanna, Xia Song
From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?
Syed Hasan Amin Mahmood, Harry Yizhou Tian, Xiaoni Duan, Chien-Ju Ho, Rajiv Khanna, Ming Yin
Sharpness-Aware Machine Unlearning
Haoran Tang, Rajiv Khanna
Membership Privacy Risks of Sharpness Aware Minimization
Young In Kim, Andrea Agiollo, Pratiksha Agrawal, Johannes O. Royset, Rajiv Khanna
Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration
Syed Hasan Amin Mahmood, Ming Yin, Rajiv Khanna
Structure-Aware Spectral Sparsification via Uniform Edge Sampling
Kaiwen He, Petros Drineas, Rajiv Khanna
A Unified Stability Analysis of SAM vs SGD: Role of Data Coherence and Emergence of Simplicity Bias
Wei-kai Chang, Rajiv Khanna
Stable Coresets via Posterior Sampling: Aligning Induced and Full Loss Landscapes
Wei-kai Chang, Rajiv Khanna
On the Support Vector Effect in DNNs: Rethinking Data Selection and Attribution
Syed Hasan Amin Mahmood, Ming Yin, Rajiv Khanna
The Space Complexity of Approximating Logistic Loss
Gregory Dexter, Petros Drineas, Rajiv Khanna
Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization
Andrea Agiollo*, Young In Kim*, Rajiv Khanna
A Precise Characterization of SGD Stability Using Loss Surface Geometry
Gregory Dexter, Borja Ocejo, Sathiya Keerthi, Aman Gupta, Ayan Acharya, Rajiv Khanna
On Memorization and Privacy risks of Sharpness Aware Minimization
Young In Kim, Pratiksha Agrawal, Johannes O Royset, Rajiv Khanna
Generalization Guarantees via Algorithm-dependent Rademacher Complexity
Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli
Fast Feature Selection with Fairness Constraints
Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers
Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael Mahoney
Geometric Rates of Convergence for Kernel-based Sampling Algorithms
Rajiv Khanna, Liam Hodgkinson, Michael Mahoney
LocalNewton: Reducing Communication Bottleneck for Distributed Learning
Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Ramchandran Kannan, Michael Mahoney
Bayesian Coresets: An Optimization Perspective
Yibo Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo
Adversarially-trained deep nets transfer better
Francisco Utrera, Evan Kravitz, N Benjamin Erichson, Rajiv Khanna, Michael W Mahoney
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method
Michal Derezinski, Rajiv Khanna, Michael W Mahoney
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
Learning Sparse Distributions using Iterative Hard Thresholding
Yibo Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo
Interpreting black box predictions using fisher kernels
Rajiv Khanna, Been Kim, Joydeep Ghosh, Oluwasanmi Koyejo
Boosting Black Box Variational Inference
Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch
Ethan Elenberg, Rajiv Khanna, Alex Dimakis, Sahand Neghaban.
Provable Accelerated Iterative Hard Thresholding
Rajiv Khanna, Anastasios Kyrillidis
Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Raetsch
Co-regularized Monotone Retargeting for Semi-supervised LeTOR
Shalmali Joshi, Rajiv Khanna, Joydeep Ghosh
Rajiv Khanna, Ethan Elenberg, Alex Dimakis, Joydeep Ghosh, Sahand Neghaban
Scalable Greedy Feature Selection via Weak Submodularity
Rajiv Khanna, Ethan Elenberg, Alex Dimakis, Sahand Neghaban, Joydeep Ghosh
Information Projection and Approximate Inference for Structured Sparse Variables
Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo
A Unified Analysis of Frank Wolfe and Matching Pursuit
Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi
A Deflation Method for Structured Probabilistic PCA
Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo
Examples are not Enough, Learn to Criticize! Criticism for Interpretability
Been Kim*, Rajiv Khanna*, Oluwasanmi Koyejo*
Rajiv Khanna, Francesco Locatello, Michael Tschannen, Martin Jaggi
S. Sathiya Keerthi, Tobias Schnabel, Rajiv Khanna
Sparse Submodular Probabilistic PCA
Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi O. Koyejo
Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo
On Prior Distributions and Approximate Inference for Structured Variables
Oluwasanmi O. Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack
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
Estimating rates of rare events with multiple hierarchies through scalable log-linear models
Deepak Agarwal, Rahul Agrawal, Rajiv Khanna, Nagaraj Kota
Translating relevance scores to probabilities for contextual advertising
Deepak Agarwal, Evgeniy Gabrilovich, Robert Hall, Vanja Josifovski, Rajiv Khanna
Structured learning for non-smooth ranking losses
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chiru Bhattacharyya