Praveen Venkatesh — accepted AI-conference papers

2023

Gaussian Partial Information Decomposition: Bias Correction and Application to High-dimensional Data

Praveen Venkatesh, Corbett Bennett, Sam Gale, Tamina K. Ramirez, Greggory Heller, Severine Durand +2

NeurIPS 2023spotlight

Recent advances in neuroscientific experimental techniques have enabled us to simultaneously record the activity of thousands of neurons across multiple brain regions. This has led to a growing need for computational tools capable of analyzing how task-relevant information is represented and communi…

Cited by 12 Source ↗PDF ↗Scholar ↗

2021

Can Information Flows Suggest Targets for Interventions in Neural Circuits?

Praveen Venkatesh, Sanghamitra Dutta, Neil Mehta, Pulkit Grover

NeurIPS 2021poster

Motivated by neuroscientific and clinical applications, we empirically examine whether observational measures of information flow can suggest interventions. We do so by performing experiments on artificial neural networks in the context of fairness in machine learning, where the goal is to induce fa…

Cited by 8 Source ↗PDF ↗code ↗Scholar ↗

2019

Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models

Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama

NeurIPS 2019poster

We propose and analyze the problems of \textit{community goodness-of-fit and two-sample testing} for stochastic block models (SBM), where changes arise due to modification in community memberships of nodes. Motivated by practical applications, we consider the challenging sparse regime, where expect…

Cited by 9 Source ↗PDF ↗Scholar ↗