53 citations · 181 across the 16 of their papers we have counts for
8 papers · 1 filter
Fast Feature Selection with Fairness Constraints
Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch +4
We study the fundamental problem of selecting optimal features for model construction. This problem is computationally challenging on large datasets, even with the use of greedy al…
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…
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 Variational Inference: an Optimization Perspective
Francesco Locatello, Rajiv Khanna, Joydeep Ghosh +1
Variational inference is a popular technique to approximate a possibly intractable Bayesian posterior with a more tractable one. Recently, boosting variational inference has been p…
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe
Francesco Locatello, Rajiv Khanna, Michael Tschannen +1
Two of the most fundamental prototypes of greedy optimization are the matching pursuit and Frank-Wolfe algorithms. In this paper, we take a unified view on both classes of methods,…
Pursuits in Structured Non-Convex Matrix Factorizations
Rajiv Khanna, Michael Tschannen, Martin Jaggi
Efficiently representing real world data in a succinct and parsimonious manner is of central importance in many fields. We present a generalized greedy pursuit framework, allowing…
Towards a Better Understanding of Predict and Count Models
S. Sathiya Keerthi, Tobias Schnabel, Rajiv Khanna
In a recent paper, Levy and Goldberg pointed out an interesting connection between prediction-based word embedding models and count models based on pointwise mutual information. Un…
Parallel Matrix Factorization for Binary Response
Rajiv Khanna, Liang Zhang, Deepak Agarwal +1
Predicting user affinity to items is an important problem in applications like content optimization, computational advertising, and many more. While bilinear random effect models (…