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Found 6 papers, 1 papers with code
Intelligent Monitoring Framework for Cloud Services: A Data-Driven Approach
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.
X-lifecycle Learning for Cloud Incident Management using LLMs
to generate insights for detection, root causing and mitigating of incidents.
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
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.
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
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.
Decentralized Langevin Dynamics for Bayesian Learning
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.
A Decentralized Approach to Bayesian Learning
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.
