28 citations · 58 across the 6 of their papers we have counts for
10 papers
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nyström method
Michał Dereziński, Rajiv Khanna, Michael W. Mahoney
The Column Subset Selection Problem (CSSP) and the Nyström method are among the leading tools for constructing small low-rank approximations of large datasets in machine learning a…
Learning Sparse Distributions using Iterative Hard Thresholding
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis +1
Iterative hard thresholding (IHT) is a projected gradient descent algorithm, known to achieve state of the art performance for a wide range of structured estimation problems, such…
Interpreting Black Box Predictions using Fisher Kernels
Rajiv Khanna, Been Kim, Joydeep Ghosh +1
Research in both machine learning and psychology suggests that salient examples can help humans to interpret learning models. To this end, we take a novel look at black box interpr…
Boosting Black Box Variational Inference
Francesco Locatello, Gideon Dresdner, Rajiv Khanna +2
Approximating a probability density in a tractable manner is a central task in Bayesian statistics. Variational Inference (VI) is a popular technique that achieves tractability by…
Scalable Greedy Feature Selection via Weak Submodularity
Rajiv Khanna, Ethan Elenberg, Alexandros G. Dimakis +2
Greedy algorithms are widely used for problems in machine learning such as feature selection and set function optimization. Unfortunately, for large datasets, the running time of e…
On Approximation Guarantees for Greedy Low Rank Optimization
Rajiv Khanna, Ethan Elenberg, Alexandros G. Dimakis +1
We provide new approximation guarantees for greedy low rank matrix estimation under standard assumptions of restricted strong convexity and smoothness. Our novel analysis also unco…