Search Results for author: Guang Zhao

Found 5 papers, 0 papers with code

Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale

no code implementations14 Nov 2023 Wei Wen, Kuang-Hung Liu, Igor Fedorov, Xin Zhang, Hang Yin, Weiwei Chu, Kaveh Hassani, Mengying Sun, Jiang Liu, Xu Wang, Lin Jiang, Yuxin Chen, Buyun Zhang, Xi Liu, Dehua Cheng, Zhengxing Chen, Guang Zhao, Fangqiu Han, Jiyan Yang, Yuchen Hao, Liang Xiong, Wen-Yen Chen

In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle.

Neural Architecture Search

Efficient Active Learning for Gaussian Process Classification by Error Reduction

no code implementations NeurIPS 2021 Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian

Moreover, as the EER is not smooth, it can not be combined with gradient-based optimization techniques to efficiently explore the continuous instance space for query synthesis.

Active Learning Classification +1

Uncertainty-aware Active Learning for Optimal Bayesian Classifier

no code implementations ICLR 2021 Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian

For pool-based active learning, in each iteration a candidate training sample is chosen for labeling by optimizing an acquisition function.

Active Learning Classification +1

Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models

no code implementations8 Jan 2019 Randy Ardywibowo, Guang Zhao, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, Xiaoning Qian

This power-efficient sensing scheme can be achieved by deciding which group of sensors to use at a given time, requiring an accurate characterization of the trade-off between sensor energy usage and the uncertainty in ignoring certain sensor signals while monitoring.

Gaussian Processes Human Activity Recognition +1

Fast Exact Computation of Expected HyperVolume Improvement

no code implementations18 Dec 2018 Guang Zhao, Raymundo Arroyave, Xiaoning Qian

The first grid-based algorithm has a complexity of $O(m\cdot n^m)$ with $n$ denoting the size of the nondominated set and $m$ the number of objectives.

Bayesian Optimization Evolutionary Algorithms

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