1 code implementation • ICML 2018 • L. Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi
Sampling methods that choose a subset of the data proportional to its diversity in the feature space are popular for data summarization.
no code implementations • NeurIPS 2016 • Tarun Kathuria, Amit Deshpande, Pushmeet Kohli
Gaussian Process bandit optimization has emerged as a powerful tool for optimizing noisy black box functions.
no code implementations • 23 Oct 2016 • L. Elisa Celis, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi
However, in doing so, a question that seems to be overlooked is whether it is possible to produce fair subsamples that are also adequately representative of the feature space of the data set - an important and classic requirement in machine learning.
no code implementations • 1 Aug 2016 • L. Elisa Celis, Amit Deshpande, Tarun Kathuria, Damian Straszak, Nisheeth K. Vishnoi
Consequently, we obtain a few algorithms of independent interest: 1) to count over the base polytope of regular matroids when there are additional (succinct) budget constraints and, 2) to evaluate and compute the mixed characteristic polynomials, that played a central role in the resolution of the Kadison-Singer problem, for certain special cases.
no code implementations • 6 Jul 2016 • Tarun Kathuria, Amit Deshpande
When pairwise similarities are captured by a kernel, the determinants of submatrices provide a measure of diversity or independence of items within a subset.