53 citations · 181 across the 16 of their papers we have counts for
5 papers · 1 filter
IHT dies hard: Provable accelerated Iterative Hard Thresholding
Rajiv Khanna, Anastasios Kyrillidis
We study --both in theory and practice-- the use of momentum motions in classic iterative hard thresholding (IHT) methods. By simply modifying plain IHT, we investigate its converg…
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…
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…
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,…