Portfolio item number 1
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Posts by Collection
portfolio
Portfolio item number 2
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publications
Chance-Constrained Programs for Link Prediction.
Published in The Conference on Neural Information Processing Systems (NIPS) Workshop, 2009
Janardhan Rao Doppa, Jun Yu, Prasad Tadepalli, and Lise Getoor
Learning Algorithms for Link Prediction based on Chance Constraints.
Published in The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2010
Janardhan Rao Doppa , Jun Yu, Prasad Tadepalli, and Lise Getoor
Modeling Experts and Novices in Citizen Science Data for Species Distribution Modeling.
Published in The 10th IEEE International Conference on Data Mining (ICDM), 2010
Jun Yu, Weng-Keen Wong, and Rebecca A. Hutchinson
Crowdsourcing Citizen Science Data Quality with a Human-Computer Learning Network.
Published in The Conference on Neural Information Processing Systems (NIPS) Workshop, 2010
Andrea Wiggins, Jeff Gerbracht, Carl Lagoze, Jun Yu, Weng-Keen Wong, and Steve Kelling
Emergent Filters: Automated Data Verification in a Large-Scale Citizen Science Project.
Published in The IEEE International Conference on eScience Workshop, 2011
Steve Kelling, Jun Yu, Jeff Gerbracht, and Weng-Keen Wong
Multi-label Classification for Species Distribution Modeling.
Published in The International Conference on Machine Learning (ICML) Workshop, 2011
Jun Yu, Weng-Keen Wong, Tom Dietterich, Julia Jones, Matthew Betts, Sarah Frey, Susan Shirley, Jeffery Miller, and Matt White
Automated Data Verification in a Large-scale Citizen Science Project: a Case Study.
Published in The IEEE International Conference on eScience (eScience), 2012
Jun Yu, Steve Kelling, Jeff Gerbracht, and Weng-Keen Wong
eBird: A Human/Computer Learning Network for Biodiversity Conservation and Research.
Published in The Twenty-fourth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI), 2012
Steve Kelling, Jeff Gerbracht, Daniel Fink, Carl Lagoze, Weng-Keen Wong, Jun Yu, Theodoros Damoulas, and Carla Gomes.
eBird: A Human/Computer Learning Network for Biodiversity Conservation and Research.
Published in AI magazine, 2013
Steve Kelling, Carl Lagoze, Weng-Keen Wong, Jun Yu, Theodoros Damoulas, Jeff Gerbracht, Daniel Fink, and Carla Gomes
Modeling Misidentification of Bird Species by Citizen Scientists.
Published in The Twenty-seventh Conference on Neural Information Processing Systems (NIPS), 2013
Jun Yu, Rebecca A. Hutchinson, and Weng-Keen Wong
Clustering Species Accumulation Curves to Identify Groups of Citizen Scientists with Similar Skill Levels.
Published in The Twenty-seventh Conference on Neural Information Processing Systems (NIPS) Workshop, 2013
Jun Yu, Weng-Keen Wong, and Steve Kelling
Machine Learning for Improving the Quality of Citizen Science Data.
Published in Ph.D Dissertation at Oregon State University, 2013
Jun Yu
A Latent Variable Model for Discovering Bird Species Commonly Misidentified by Citizen Scientists.
Published in The Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2014
Jun Yu, Rebecca Hutchinson, and Weng-Keen Wong
HC-Search for Multi-Label Prediction: An Empirical Study.
Published in The Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2014
Janardhan Rao Doppa, Jun Yu, Chao Ma, Alan Fern and Prasad Tadepalli
The eBird enterprise: an integrated approach to the development and application of citizen science.
Published in Biological Conservation, 2014
Clustering Species Accumulation Curves to Identify Skill Levels of Citizen Scientists Participating in the eBird Project.
Published in the Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI), 2014
Jun Yu, Weng-Keen Wong, and Steve Kelling
Latent Dirichlet Allocation based Diversified Retrieval for E-commerce Search.
Published in The 7th ACM International Conference on Web Search and Data Mining Conference (WSDM), 2014
Jun Yu, Sunil Mohan, Duangmanee (Pew) Putthividhya, and Weng-Keen Wong
Bayesian and empirical Bayesian forests.
Published in The 32nd International Conference on Machine Learning (ICML), 2015
Matt Taddy, Chun-Sheng Chen, Jun Yu, and Mitch Wyle
Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves?
Published in PLoS One, 2015
Graph Neural Networks for Friend Ranking in Large-scale Social Platforms.
Published in Proceedings of the Web Conference (WWW), 2021
Aravind Sankar, Yozen Liu, Jun Yu, and Neil Shah
Embedding Based Retrieval in Friend Recommendation.
Published in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023
Jiahui Shi, Vivek Chaurasiya, Yozen Liu, Shubham Vij, Yan Wu, Satya Kanduri, Neil Shab, Peicheng Yu, Nik Srivastava, Lei Shi, Ganesh Venkataraman, and Jun Yu
Lessons Learnt from Building Friend Recommendation Systems.
Published in The 17th ACM International Conference on Web Search and Data Mining Conference (WSDM), 2024
Jun Yu
Improving Embedding-Based Retrieval in Friend Recommendation with ANN Query Expansion.
Published in The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2024
Pau Kung, Zihao Fan, Tong Zhao, Yozen Liu, Zhixin Lai, Jiahui Shi, Yan Wu, Jun Yu, Neil Shah, Ganesh Venkataraman
talks
Latent Dirichlet Allocation-based Diversified Retrieval for E-commerce Search.
Published:
Diversified retrieval is a very important problem on many e-commerce sites, e.g. eBay and Amazon. Using IR approaches without optimizing for diversity results in a clutter of redundant items that belong to the same products. Most existing product taxonomies are often too noisy, with overlapping structures and non-uniform granularity, to be used directly in diversified retrieval. To address this problem, we propose a Latent Dirichlet Allocation (LDA) based diversified retrieval approach that selects diverse items based on the hidden user intents.
Bayesian and empirical Bayesian forests.
Published:
We derive ensembles of decision trees through a nonparametric Bayesian model, allowing us to view random forests as samples from a posterior distribution. This insight provides large gains in interpretability, and motivates a class of Bayesian forest (BF) algorithms that yield small but reliable performance gains. Based on the BF framework, we are able to show that high-level tree hierarchy is stable in large samples. This leads to an empirical Bayesian forest (EBF) algorithm for building approximate BFs on massive distributed datasets and we show that EBFs outperform subsampling based alternatives by a large margin.
Bidding Strategy for Google Product Listing Ads in eBay Paid Search.
Published:
In this talk, I provide an overview of building large-scale machine learning models to optimize the bids for hundreds of millions of eBay listings sent to Google Product Listing Ads (PLA).
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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