1 citations · 1 across the 9 of their papers we have counts for
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Online Adaptive Kernel Mixing for Gaussian Process Decision Making
Kavin Aravindan, Mani Tej Sriram, Gautam Dasarathy +1
Gaussian Processes (GPs) are widely used as surrogates for black-box functions in sequential decision-making problems such as Bayesian optimization (BO), level set estimation (LSE)…
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…
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…
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…
Advanced Tutorial: Label-Efficient Two-Sample Tests
Weizhi Li, Visar Berisha, Gautam Dasarathy
Hypothesis testing is a statistical inference approach used to determine whether data supports a specific hypothesis. An important type is the two-sample test, which evaluates whet…
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…
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…