8 citations · 26 across the 10 of their papers we have counts for
10 papers
Subsampled Ensemble Can Improve Generalization Tail Exponentially
Huajie Qian, Donghao Ying, Henry Lam +1
Ensemble learning is a popular technique to improve the accuracy of machine learning models. It traditionally hinges on the rationale that aggregating multiple weak models can lead…
AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes
Zhiqiang Zhou, Chaoli Zhang, Lingna Ma +6
The existing resource allocation policy for application instances in Kubernetes cannot dynamically adjust according to the requirement of business, which would cause an enormous wa…
RobustScaler: QoS-Aware Autoscaling for Complex Workloads
Huajie Qian, Qingsong Wen, Liang Sun +3
Autoscaling is a critical component for efficient resource utilization with satisfactory quality of service (QoS) in cloud computing. This paper investigates proactive autoscaling…
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms
Yingying Zhang, Zhengxiong Guan, Huajie Qian +7
As business of Alibaba expands across the world among various industries, higher standards are imposed on the service quality and reliability of big data cloud computing platforms…
Learning Prediction Intervals for Regression: Generalization and Calibration
Haoxian Chen, Ziyi Huang, Henry Lam +2
We study the generation of prediction intervals in regression for uncertainty quantification. This task can be formalized as an empirical constrained optimization problem that mini…
Combating Conservativeness in Data-Driven Optimization under Uncertainty: A Solution Path Approach
Henry Lam, Huajie Qian
In data-driven optimization, solution feasibility is often ensured through a "safe" reformulation of the uncertain constraints, such that an obtained data-driven solution is guaran…
Subsampling to Enhance Efficiency in Input Uncertainty Quantification
Henry Lam, Huajie Qian
In stochastic simulation, input uncertainty refers to the output variability arising from the statistical noise in specifying the input models. This uncertainty can be measured by…
Bounding Optimality Gap in Stochastic Optimization via Bagging: Statistical Efficiency and Stability
Henry Lam, Huajie Qian
We study a statistical method to estimate the optimal value, and the optimality gap of a given solution for stochastic optimization as an assessment of the solution quality. Our ap…
Optimization-based Quantification of Simulation Input Uncertainty via Empirical Likelihood
Henry Lam, Huajie Qian
We study an optimization-based approach to construct statistically accurate confidence intervals for simulation performance measures under nonparametric input uncertainty. This app…
Optimization-based Calibration of Simulation Input Models
Aleksandrina Goeva, Henry Lam, Huajie Qian +1
Studies on simulation input uncertainty often built on the availability of input data. In this paper, we investigate an inverse problem where, given only the availability of output…