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20222024
most citedA Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud

48 citations · 63 across the 7 of their papers we have counts for

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Showing cs.LGShow all

5 papers · 1 filter

cs.LG2024★ 2 cited

GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting

Fan Zhou, Chen Pan, Lintao Ma +9

Time series forecasts of different temporal granularity are widely used in real-world applications, e.g., sales prediction in days and weeks for making different inventory plans. H…

cs.LG2023

Automatic Deduction Path Learning via Reinforcement Learning with Environmental Correction

Shuai Xiao, Chen Pan, Min Wang +6

Automatic bill payment is an important part of business operations in fintech companies. The practice of deduction was mainly based on the total amount or heuristic search by divid…

cs.LG2023★ 1 cited

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

Fan Zhou, Chen Pan, Lintao Ma +9

Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities,…

cs.LG2022★ 10 cited

End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation

Shiyu Wang, Fan Zhou, Yinbo Sun +3

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconc…

cs.LG2022★ 48 cited

A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud

Siqiao Xue, Chao Qu, Xiaoming Shi +11

Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating w…