Search Results for author: Fangqiu Han

Found 4 papers, 1 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

DistDNAS: Search Efficient Feature Interactions within 2 Hours

no code implementations1 Nov 2023 Tunhou Zhang, Wei Wen, Igor Fedorov, Xi Liu, Buyun Zhang, Fangqiu Han, Wen-Yen Chen, Yiping Han, Feng Yan, Hai Li, Yiran Chen

To optimize search efficiency, DistDNAS distributes the search and aggregates the choice of optimal interaction modules on varying data dates, achieving over 25x speed-up and reducing search cost from 2 days to 2 hours.

Recommendation Systems

mvn2vec: Preservation and Collaboration in Multi-View Network Embedding

1 code implementation19 Jan 2018 Yu Shi, Fangqiu Han, Xinwei He, Xinran He, Carl Yang, Jie Luo, Jiawei Han

With experiments on a series of synthetic datasets, a large-scale internal Snapchat dataset, and two public datasets, we confirm the validity and importance of preservation and collaboration as two objectives for multi-view network embedding.

Network Embedding

Collective Decision Dynamics in Group Evacuation: Behavioral Experiment and Machine Learning Models

no code implementations17 Jun 2016 Chantal Nguyen, Fangqiu Han, Kimberly J. Schlesinger, Izzeddin Gür, Jean M. Carlson

Identifying factors that affect human decision making and quantifying their influence remain essential and challenging tasks for the design and implementation of social and technological communication systems.

BIG-bench Machine Learning Decision Making

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