593 citations · 1.9k across the 44 of their papers we have counts for
5 papers · 1 filter
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,…
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…
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…
BI-MAML: Balanced Incremental Approach for Meta Learning
Yang Zheng, Jinlin Xiang, Kun Su +1
We present a novel Balanced Incremental Model Agnostic Meta Learning system (BI-MAML) for learning multiple tasks. Our method implements a meta-update rule to incrementally adapt i…
R-FORCE: Robust Learning for Random Recurrent Neural Networks
Yang Zheng, Eli Shlizerman
Random Recurrent Neural Networks (RRNN) are the simplest recurrent networks to model and extract features from sequential data. The simplicity however comes with a price; RRNN are…