8 papers
Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics
Vignesh Tirukkonda, Gautam Dasarathy
Gaussian graphical model selection is usually studied under independent sampling, but in many applications the data arise as a single trajectory of a dependent stochastic process.…
Stochastic Linear Bandits with Partially Observed Actions
Gautam Dasarathy, Vineet Gattani, Lalit Jain
The stochastic linear bandit, where actions are represented as vectors and rewards are linear, is a central paradigm for sequential decision making. We study a partially observed v…
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…
Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Health
Pavel Dolin, Weizhi Li, Gautam Dasarathy +1
This position paper argues that post-deployment monitoring in clinical AI is underdeveloped and proposes statistically valid and label-efficient testing frameworks as a principled…
Matched-Pair Experimental Design with Active Learning
Weizhi Li, Gautam Dasarathy, Visar Berisha
Matched-pair experimental designs aim to detect treatment effects by pairing participants and comparing within-pair outcome differences. In many situations, the overall effect size…
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…