Author

Gautam Dasarathy

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#machine learning Sep 2026

Online adaptive kernel mixing for Gaussian process decision making

This work introduces HACK GPs (Hedge Adaptive Cumulative Kernels), a method that views kernel selection as an online learning with expert advice problem and establishes general guarantees showing that, under a loss-gap condition, the weight concentrates on the best kernel and the resulting acquisition function is close...

Kavin Aravindan, Mani Tej Sriram, Gautam Dasarathy et al. · Performance evaluation (Prin... · 0 citations

Preprint Jul 2026

Stochastic Linear Bandits with Partially Observed Actions

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 variant of this problem in which the learning agent only sees a random subset of coordinates for each action. Such partial observability aris...

Gautam Dasarathy, V. Gattani, Lalit Jain · 0 citations

Jul 2026

Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics

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. We study exact recovery of the graph from one trajectory of random-scan Gaussian Glauber dynamics. Existing techniques for this problem eith...

Vignesh Tirukkonda, Gautam Dasarathy · arXiv.org · 0 citations