Jianbo Ye

Ph.D. student, 2013 - 2018 (expected)
jxy198 [at] ist.psu.edu

Room 310, IST Building
Penn State University
University Park, PA 16802

Bio: Jianbo Ye is a Ph.D. candidate at College of Information Science and Technology, The Pennsylvania State University. He works with Prof. James Z. Wang and Prof. Jia Li. His research interests are machine learning, applied optimization, computational statistics and deep learning. His thesis has been focused on developing scalable and robust numerical algorithms that apply optimal transport theory and Wasserstein geometry to machine learning models. He received the B.Sc. degree in Mathematics from University of Science and Technology of China (USTC). He was a research postgraduate at The University of Hong Kong before joining Penn State as a PhD student. He has worked as a research intern at Intel (2013) and Adobe (2017).


Keywords: all selected optimization learning pattern recognition optimal transport


Leveraging Long and Short-term Information in Content-aware Movie Recommendation
Wei Zhao, Benyou Wang, Jianbo Ye, Yong Gao, Min Yang, Zhou Zhao, Xiaojun Chen, (arXiv:1712.09059 [cs.IR], December 2017)

A Faster Drop-in Implementation for Leaf-wise Exact Greedy Decision Tree Induction Using Pre-sorted Deque
Jianbo Ye (arXiv:1712.06989 [cs.DS], December 2017)
code learning

Aggregated Wasserstein Metric and State Registration for Hidden Markov Models
Yukun Chen, Jianbo Ye, Jia Li (arXiv:1711.05792 [cs.LG], November 2017)
pattern recognition optimal transport

Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers
Jianbo Ye, Xin Lu, Zhe Lin, and James Z. Wang, International Conference on Learning Representations (ICLR), 2018 under open review
learning optimization

Peer-reviewed Papers


Investigating Active Learning for Concept Prerequisite Learning
Chen Liang, Jianbo Ye, Shuting Wang, Bart Pursel, C. Lee Giles, The AAAI 8th Symposium on Educational Advances in Artifical Intelligence (EAAI), New Orleans, Lousiana, 2018
To appear learning


Dual Learning for Cross-domain Image Captioning
Wei Zhao, Wei Xu, Min Yang, Jianbo Ye, Zhou Zhao, Yabing Feng, and Yu Qiao, ACM International Conference on Information and Knowledge Management (CIKM), Singapore, November 2017
pdf g-scholar learning

A Simulated Annealing based Inexact Oracle for Wasserstein Loss Minimization
Jianbo Ye, James Z. Wang and Jia Li, International Conference on Machine Learning (ICML), Sydney, Australia, August 2017 (arXiv:1608.03859 [stat.CO], August 2016)
pdf & supp g-scholar video optimization learning optimal transport

Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering
Jianbo Ye, Yanran Li, Zhaohui Wu, James Z. Wang, Wenjie Li, Jia Li, Proceedings of The Annual Meeting of the Association for Computational Linguistics (ACL), Vancouver, Canada, July 2017. Long paper.
pdf g-scholar code dataset learning optimal transport

Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data
Jianbo Ye, Jia Li, Michelle G. Newman, Reginald B. Adams, Jr., James Z. Wang, IEEE Transactions on Affective Computing, 2017 (arXiv:1701.01096 [stat.ML], January 2017)
pdf g-scholar code dataset g-scholar learning

This project also develops a scalable data analytic tool, called accelerated D2-clustering, to process large scale distribution data. It could potentially leverage hundreds of CPUs with a very decent scaling efficiency (say, 70-80%).

If you are a government agency, an education institution, or a non-profit organization, we may offer you a FREE academic license of the C/MPI package to run on clusters. Please contact authors by email to discuss details. If you are commercial and would like to use our software, let us know and we will try to arrange to let you use.

Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support
Jianbo Ye, Panruo Wu, James Z. Wang and Jia Li, IEEE Transactions on Signal Processing, January 2017 (arXiv:1510.00012 [stat.CO], September 2015)
pdf g-scholar code optimization learning optimal transport

Recovering Concept Prerequisite Relations From University Course Dependencies
Chen Liang, Jianbo Ye, Zhaohui Wu, Bart Pursel, C. Lee Giles, The AAAI 7th Symposium on Educational Advances in Artifical Intelligence (EAAI), San Francisco, California, 2017
pdf g-scholar dataset preprint learning


A Distance for HMMs based on Aggregated Wasserstein Metric and State Registration
Yukun Chen, Jianbo Ye, Jia Li, European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, October 2016
Spotlight Presentation pdf g-scholar code pattern recognition optimal transport

R-BiHDM(-s)[YeYY13], which has been used for SHREC14 human track, is a simple, unsupervised, data independent method. (rank among the best 3 in scanned human dataset)
Shape Retrieval of Non-Rigid 3D Human Models
D. Pickup, et al., International Journal on Computer Vision, April 2016, Springer
pdf g-scholar pattern recognition (benchmark paper, earlier version appeared in EG Workshop 3DOR'14)

2013 ~ 2015

A Fast Modal Space Transform For Robust Nonrigid Shape Retrieval
Jianbo Ye and Yizhou Yu, The Visual Computer, March 2015, Springer
pdf g-scholar pattern recognition (extended version of the ICMR'13 paper)

Scaling Up Discrete Distribution Clustering Using ADMM
Jianbo Ye and Jia Li, IEEE International Conference on Image Processing (ICIP), Paris, France, October 2014
pdf g-scholar optimization optimal transport

State-of-the-art unsupervised method to obtain a global nonrigid shape signature for shape retrieval and comparison.
Fast Nonrigid 3D Retrieval Using Modal Space Transform
Jianbo Ye, Zhicheng Yan, and Yizhou Yu, ACM International Conference on Multimedia Retrieval (ICMR), Dallas, April 2013 (Oral, 17.7% acceptance rate)
pdf g-scholar project page dataset slides pattern recognition

Working Papers

Yet another model reduction technique for deformable meshes based on approximation quality controllable subspace.
On the Approximation Theory of Linear Variational Subspace Design
Jianbo Ye and Zhixin Yan, arXiv:1506.08459 [cs.GR], June 2015
pdf g-scholar gitxiv software executable demo: linux-x86_64 video (40M) optimization


Sep 2017, Optimal transport for machine learning: the state-of-the-art numerical tools, Artificial Intelligence Seminar Series, sponsored by Apple, CMU. website
Aug 2017, Oral presentation at ICML, Sydney. video
May 2017, New numerical tools for optimal transport and their machine learning applications BIRS-CMO Workshop (Optimal Transport meets Probability, Statistics and Machine Learning), Oaxaca. video
Oct 2015, Accelerated Discrete Distribution Clustering under Wasserstein Distance
Apr 2014, Probabilistic Graphical Models and Their Applications in Vision and Graphics
Oct 2013, Emerging Technologies in Computer Graphics


[2015-2016] AD2-Clustering: a parallel clustering algorithm for discrete distributions, including normalized histogram as a special case, under the Wasserstein metric. software (to appear)

[2014-2015] neuron: I re-implemented a full-fledged Scala library for composing and training neural network of complex topologies with parameter sharing, supporting different activations, metrics, regularization, and optimization methods. It also includes different variants of multilayer perceptron and auto-encoders. project page
Scala breeze

[2014-2015] dmfCramer: Discrete martrix factorizatin with Cramer risk minimization, which is yet another probabilistic matrix factorization method with loss function based on large deviations theory rather than conventional MLE/MAP framework. project page
Scala breeze

[2013] Pocket Avatar (intern project at Intel): I developed an efficient data-driven framework for real-time facial expression retargeting. demo

[2012-2013] iMeshDeform: A C++ mesh deformation framework based on linear variational subspace. project page video

[2011-2012] R-BiHDM: state-of-the-art, simple and fast signature for nonrigid shapes, which have been tested upon multiple benchmarks. project page