Neural Network Attributions: A Causal Perspective

1 code implementation • 6 Feb 2019 • Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N. Balasubramanian

We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such).

MMD-Regularized Unbalanced Optimal Transport

1 code implementation • 10 Nov 2020 • Piyushi Manupriya, J. Saketha Nath, Pratik Jawanpuria

Further, for real-world applications involving non-discrete measures, we present an estimator for the transport plan that is supported only on the given ($m$) samples.

Improving Attribution Methods by Learning Submodular Functions

1 code implementation • 19 Apr 2021 • Piyushi Manupriya, Tarun Ram Menta, J. Saketha Nath, Vineeth N Balasubramanian

This work explores the novel idea of learning a submodular scoring function to improve the specificity/selectivity of existing feature attribution methods.

Specificity

Consistent Optimal Transport with Empirical Conditional Measures

1 code implementation • 25 May 2023 • Piyushi Manupriya, Rachit Keerti Das, Sayantan Biswas, Saketha Nath Jagarlapudi

Given samples from two joint distributions, we consider the problem of Optimal Transportation (OT) between them when conditioned on a common variable.