32 citations · 33 across the 6 of their papers we have counts for
6 papers
Exponential Convergence of (Stochastic) Gradient Descent for Separable Logistic Regression
Sacchit Kale, Piyushi Manupriya, Pierre Marion +2
Gradient descent and stochastic gradient descent are central to modern machine learning, yet their behavior under large step sizes remains theoretically unclear. Recent work sugges…
Submodular Framework for Structured-Sparse Optimal Transport
Piyushi Manupriya, Pratik Jawanpuria, Karthik S. Gurumoorthy +2
Unbalanced optimal transport (UOT) has recently gained much attention due to its flexible framework for handling un-normalized measures and its robustness properties. In this work,…
Consistent Optimal Transport with Empirical Conditional Measures
Piyushi Manupriya, Rachit Keerti Das, Sayantan Biswas +1
Given samples from two joint distributions, we consider the problem of Optimal Transportation (OT) between them when conditioned on a common variable. We focus on the general setti…
Improving Attribution Methods by Learning Submodular Functions
Piyushi Manupriya, Tarun Ram Menta, J. Saketha Nath +1
This work explores the novel idea of learning a submodular scoring function to improve the specificity/selectivity of existing feature attribution methods. Submodular scores are na…
MMD-Regularized Unbalanced Optimal Transport
Piyushi Manupriya, J. Saketha Nath, Pratik Jawanpuria
We study the unbalanced optimal transport (UOT) problem, where the marginal constraints are enforced using Maximum Mean Discrepancy (MMD) regularization. Our work is motivated by t…
Neural Network Attributions: A Causal Perspective
Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar +1
We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architectur…