Hi. I'm Yixuan Li (pronounced as e-shwen). I am currently a PhD candidate in Electrical and Computer Engineering at Cornell University, advised by John E. Hopcroft. The goal of my thesis research is to develop computational foundations and practical advances for scaling machine learning methods on web-scale data. I have pursued research on both principled and applied aspects of machine perception, learning and reasoning.

I am particularly interested in large-scale machine learning for the web, with topics including scalable semi-supervised learning, deep representation learning for vision tasks, user modeling in social media, and visual attention based personalization etc. A key focus of my recent work has been on deep learning. Projects include convergent learning in deep neural networks, optimizing neural networks with efficient computational cost, and adversarial training of deep generative models.

Prior to coming to Cornell, I graduated from Shanghai Jiaotong University with B.Eng in Information Engineering in 2013. I spent two summers at Google (Research) Mountain View in 2015 and 2016.

Update (3/12/2017): Received ICLR 2017 Student Travel Award.
Update (2/27/2016): Paper on StackedGAN has been accepted into CVPR 2017.
Update (2/6/2017): Paper on Snapshot Ensembles has been accepted into ICLR 2017.
Update (12/20/2016): My summer internship paper at Google is invited to the industrial track in WWW 2017.
Update (2/5/2016): I will be interning at Machine Intelligence at Google Research (Mountain View) for the summer. I am very excited about it!
Update (2/4/2016): Paper on Convergent Learning has been accpeted for oral presentation (5.7%) in ICLR 2016! (check out preprint here)

[My CV] [Google Scholar]

Research Highlights

Snapshot Ensembles: Train 1 Get M for Free.
Gao Huang*, Yixuan Li*, Geoff Pleiss, Zhuang Liu, John Hopcroft, Kilian Weinberger. (* equal contribution)
Training multiple deep networks for model averaging is computationally expensive. In this paper, we propose a method to obtain the seemingly contradictory goal to obtain ensembles of multiple neural network at no additional training cost. We achieve this goal by letting a single neural network converge into several local minima along its optimization path and save the model parameters. To obtain repeated rapid convergence we leverage recent work on cyclic learning rate schedules. The resulting technique, which we refer to as Snapshot Ensembling, is surprisingly simple, yet effective. On Cifar-10 and Cifar-100 our DenseNet Snapshot Ensembles obtain error rates of 3.4% and 17.4% respectively.
Convergent Learning: Do different neural networks learn the same representations?
Yixuan Li*, Jason Yosinski*, Jeff Clune, Hod Lipson and John Hopcroft. (* equal contribution)
In this paper we investigate the extent to which neural networks exhibit what we call convergent learning, which is when the representations learned by multiple nets converge to a set of features which are either individually similar between networks or where subsets of features span similar low-dimensional spaces. We propose a specific method of probing representations: training multiple networks and then comparing and contrasting their individual, learned representations at the level of neurons or groups of neurons. This initial investigation reveals a few previously unknown properties of neural networks. The insights described here include (1) that some features are learned reliably in multiple networks, yet other features are not consistently learned; (2) units learn to span low-dimensional subspaces and, while these subspaces are common to multiple networks, the specific basis vectors learned are not.
Stacked Generative Adversarial Networks
Xun Huang, Yixuan Li, Omid Poursaeed, John Hopcroft, Serge Belongie.
In this paper we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a discriminative bottom-up deep network. Our model consists of a top-down stack of GANs, each trained to generate "plausible" lower-level representations, conditioned on higher-level representations. A representation discriminator is introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom-up discriminative network, providing intermediate supervision. Unlike the original GAN that uses a single noise vector to represent all the variations, our SGAN decomposes variations into multiple levels and gradually resolves uncertainties in the top-down generative process. Experiments demonstrate that SGAN is able to generate diverse and high-quality images, as well as being more interpretable than a vanilla GAN.
In a World That Counts: Clustering and Detecting Fake Social Engagement at Scale
Yixuan Li, Oscar Martinez, Xing Chen, Yi Li, John Hopcroft.
How can web services that depend on user generated content discern fake social engagement activities by spammers from legitimate ones? In this paper, we focus on the social site of YouTube and the problem of identifying bad actors posting inorganic contents and inflating the count of social engagement metrics. We propose an effective method, Leas (Local Expansion at Scale), and show how the fake engagement activities on YouTube can be tracked over time by analyzing the temporal graph based on the engagement behavior pattern between users and YouTube videos. We demonstrate the effectiveness of our deployment at Google by achieving an manual review accuracy of 98% on YouTube Comments graph in practice. Leas is actively in use at Google, searching for daily deceptive practices on YouTube's engagement graph spanning over a billion users.
The Lifecycle and Cascade of WeChat Social Messaging Groups.
Jiezhong Qiu, Yixuan Li, Jie Tang, Zheng Lu, Hao Ye, Bo Chen, Qiang Yang and John Hopcroft.
In this paper, we analyze the daily usage logs from WeChat group messaging platform - the largest standalone messaging communication service in China - with the goal of understanding the processes by which social messaging groups come together, grow new members, and evolve over time. To model the diffusion process by which groups gain new members, we study both group-level features and individual-level attributes of group members. By considering members' historical engagement behavior as well as the local social network structure that they embedded in, we develop a membership cascade model and demonstrate the effectiveness by achieving AUC of 95.31% in predicting inviter, and an AUC of 98.66% in predicting invitee.
  • WWW 2016 (Oral Presentation)
  • PDF
Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach.
Yixuan Li, Kun He, David Bindel, John Hopcroft.
Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms on mining communities have focused on the global structure, and often run in time functional to the size of the entire graph. In this paper, we propose a novel approach for finding overlapping communities called LEMON (Local Expansion via Minimum One Norm). Different from PageRank-like diffusion methods, LEMON finds the community by seeking a sparse vector in the span of the local spectra such that the seeds are in its support. We show that LEMON can achieve the highest detection accuracy among state-of-the-art proposals. The running time depends on the size of the community rather than that of the entire graph.

Publications

  • Towards Measuring and Inferring User Interest From Gaze.
    Yixuan Li, Pingmei Xu, Dmitry Lagun and Vidhya Navalpakkam
    Accepted to the 26th International Conference on World Wide Web (WWW 2017). [PDF]

  • Snapshot Ensembles: Train 1, Get M for Free.
    Gao Huang*, Yixuan Li*, Geoff Pleiss, Zhuang Liu, John Hopcroft and Kilian Weinberger
    Proceedings of International Conference on Learning Representation (ICLR 2017).
    Toulon, France, April 24 - 26, 2017.[PDF][code]

  • Stacked Generative Adversarial Networks.
    Xun Huang, Yixuan Li, Omid Poursaeed, John Hopcroft and Serge Belongie
    Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR 2017).
    Honolulu, Hawaii, July 22 - 25, 2017.[PDF][code]

  • Convergent Learning: Do different neural networks learn the same representations?
    Yixuan Li*, Jason Yosinski*, Jeff Clune, Hod Lipson and John Hopcroft
    Proceedings of International Conference on Learning Representation (ICLR 2016).
    San Juan, Puerto Rico, May 2016 (Oral presentation 5.7%) [PDF][code][video]

  • In a World that Counts: Clustering and Detecting Fake Social Engagement at Scale.
    Yixuan Li, Oscar Martinez, Xing Chen, Yi Li and John Hopcroft
    Proceedings of the 25th International World Wide Web Conference (WWW 2016).
    Montreal, Canada, April 2016. [PDF][news coverage]

  • The Lifecycle and Cascade of WeChat Social Messaging Groups.
    Jiezhong Qiu, Yixuan Li, Jie Tang, Zheng Lu, Hao Ye, Bo Chen, Qiang Yang and John Hopcroft
    Proceedings of the 25th International World Wide Web Conference (WWW 2016)
    Montreal, Canada, April 2016. [PDF]

  • Deep Manifold Traversal: Changing Labels with Convolutional Features.
    Jacob Gardner*, Paul Upchurch*, Matt Kusner, Yixuan Li, Kilian Weinberger and John Hopcroft
    Preprint on arXiv. November, 2015. [PDF]

  • Scalable and Robust Local Community Detection via Adaptive Subgraph Extraction and Diffusions.
    Kyle Kloster and Yixuan Li
    Preprint on arXiv, cs.SI:1611.05152, 2016. [PDF]

  • Overlapping Community Detection via Local Spectral Clustering.
    Yixuan Li, Kun He, David Bindel and John Hopcroft
    Preprint on arXiv, cs.SI:1509.07996, 2015. [PDF]

  • Detecting Overlapping Communities from Local Spectral Subspaces.
    Kun He, Yiwei Sun, David Bindel, John Hopcroft, Yixuan Li
    IEEE International Conference on Data Mining (ICDM 2015)
    Atlantic City, NJ, USA. November, 2015 (acceptance ratio: 18.2%) [pdf][full version]

  • Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach.
    Yixuan Li, Kun He, David Bindel, John Hopcroft
    Proceedings of the 24th International World Wide Web Conference (WWW 2015)
    Florence, Italy. May, 2015 (acceptance ratio: 14.1%) [PDF][code]

  • Multicast Capacity with Max-Min Fairness for Heterogeneous Networks.
    Yixuan Li, Qiuyu Peng, Xinbing Wang
    In IEEE/ACM Transactions on Networking (TON), 2014. [PDF]

  • On Multicast Capacity and Delay in Cognitive Radio Mobile Ad-hoc Networks.
    Jinbei Zhang, Yixuan Li, Zhuotao Liu, Fan Wu, Feng Yang, Xinbing Wang
    IEEE Transactions on Wireless Communications, 2015. [PDF]

  • Awards

  • Cornell Graduate Student Conference Grant (WWW’15, ICDM’15), Cornell University, 2015.

  • Cornell University Graduate Fellowship, Graduate School of Cornell University, 2013.

  • Academic Excellence Scholarship (5%), Shanghai Jiao Tong University, 2011 - 2013.

  • National Scholarship of China (3%), Ministry of Education of the People's Republic of China, 2012 - 2013.

  • Meritorious Winner in the American Interdisciplinary Contest in Modeling, COMAP, the Consortium for Mathematics and Its Applications, 2012.

  • First Prize in Chinese Undergraduate Mathematical Contest in Modeling, China Society for Industrial and Applied Mathematics (CSIAM), 2011.

  • Undergraduate Scholarship for Studying Abroad, Shanghai Jiaotong University, 2011.

  • Wen-Yuan Pan Scholarship (1 out of 105), 2010.

  • Work Experiences

  • PhD Research Intern, Google Inc. Mountain View, CA, 2015.5 - 2015.8

  • PhD Research Intern, Google Inc. Mountain View, CA, 2016.5 - 2016.8

  • Talks

  • Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach.
    Internaltional World Wide Web Conference (WWW 2015), Florence, Italy, 2015.5.20 [slides]

  • Convergent Learning: Do different neural networks learn the same representations?
    Cornell Machine Learning Discussion Group (MLDG), Ithaca, NY, 2015.12.2 [slides]

  • Convergent Learning: Do different neural networks learn the same representations?
    NIPS'15 Workshop on Feature Extraction, Montreal, Canada, 2015.12.11 [slides][video]

  • Local Spectral Graph Clustering at Scale: Principle and Its Application.
    Invited Talk at Google Research NYC, 2016.2.9 [slides]

  • Convergent Learning: Do different neural networks learn the same representations?
    Invited Talk at Cornell Statistics Student Seminar, 2016.3.22 [slides]

  • In a World That Counts: Clustering and Detecting Fake Social Engagement at Scale.
    Internaltional World Wide Web Conference (WWW 2016), Montreal, Canada, 2016.4.13 [slides]

  • Convergent Learning: Do different neural networks learn the same representations?
    Guest Lecture CS4850 Mathematical Foundations for the Information Age, 2016.5.6 [slides]

  • Scale, Improve and Understand Learning Through Subspace Embedding.
    A-Exam (thesis proposal exam), 2016.8.22 [slides]

  • Towards Understanding, Improving and Scaling Learning in Deep Neural Networks.
    Invited Talk at Computer Vision Group @ Cornell Tech, 2017.1.25 [slides]

  • Teaching

  • SP15 CS4850 Mathematical Foundations for the Information Age.

  • Misc

    Here are the two student organizations at Cornell I actively involve in:

  • Vice President, Chinese Students and Scholars Association at Cornell (CSSA). SP15 - SP16

  • Vice President, Technology Entrepreneurship at Cornell (TEC) . FA14 - FA16

  • Cornell at a Glance

    Photos by Yixuan

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