1 citations · 1 across the 9 of their papers we have counts for
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Local and Mixing-Based Algorithms for Gaussian Graphical Model Selection from Glauber Dynamics
Vignesh Tirukkonda, Anirudh Rayas, Gautam Dasarathy
Gaussian graphical model selection is usually studied under independent sampling, but in many applications observations arise from dependent dynamics. We study structure learning w…
Learning Networks from Wide-Sense Stationary Stochastic Processes
Anirudh Rayas, Jiajun Cheng, Rajasekhar Anguluri +2
Complex networked systems driven by latent inputs are common in fields like neuroscience, finance, and engineering. A key inference problem here is to learn edge connectivity from…
Communication-Efficient Federated Learning over Wireless Channels via Gradient Sketching
Vineet Sunil Gattani, Junshan Zhang, Gautam Dasarathy
Large-scale federated learning (FL) over wireless multiple access channels (MACs) has emerged as a crucial learning paradigm with a wide range of applications. However, its widespr…
Unraveling overoptimism and publication bias in ML-driven science
Pouria Saidi, Gautam Dasarathy, Visar Berisha
Machine Learning (ML) is increasingly used across many disciplines with impressive reported results. However, recent studies suggest published performance of ML models are often ov…