no code implementations • 29 Feb 2024 • Pooja Srinivas, Fiza Husain, Anjaly Parayil, Ayush Choure, Chetan Bansal, Saravan Rajmohan
We conduct an extensive empirical study and derive key insights on the major classes of monitors employed by cloud services at Microsoft, their associated dimensions, and the interrelationship between service properties and this ontology.
no code implementations • 15 Feb 2024 • Drishti Goel, Fiza Husain, Aditya Singh, Supriyo Ghosh, Anjaly Parayil, Chetan Bansal, Xuchao Zhang, Saravan Rajmohan
to generate insights for detection, root causing and mitigating of incidents.
no code implementations • 28 Jan 2022 • Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian Sadler, Pratap Tokekar, Alec Koppel
Doing so incurs a persistent bias that appears in the attenuation rate of the expected policy gradient norm, which is inversely proportional to the radius of the action space.
no code implementations • 15 Jun 2021 • Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel
To close this gap, we step towards persistent exploration in continuous space through policy parameterizations defined by distributions of heavier tails defined by tail-index parameter alpha, which increases the likelihood of jumping in state space.
no code implementations • NeurIPS 2020 • Anjaly Parayil, He Bai, Jemin George, Prudhvi Gurram
Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex setting.
1 code implementation • 14 Jul 2020 • Anjaly Parayil, He Bai, Jemin George, Prudhvi Gurram
Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex setting.