Publications

    • "Learning Deep ResNet Blocks Sequentially using Boosting Theory", by F. Huang, J. Ash, J. Langford and R. Schapire. preprint 2017. Download: PDF.
    • "Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition"by F. Huang and A. Anandkumar, preprint 2016. Download: PDF.

    • "Non-negative Factorization of the Occurrence Tensor from Financial Contracts"by Z. Xu, F. Huang, L. Raschid and T. Goldstein, Tensor-Learn NIPS workshop 2016. Download: PDF.   

    • "Unsupervised Learning of Transcriptional Regulatory Network via Latent Tree Graphical Models", by A. Gitter, F. Huang, R. Valluvan, E. Fraenkel, A. Anandkumar. Preprint 2016. Download: PDF.
    • "Distributed Latent Dirichlet Allocation on Spark via Tensor Decomposition", by Furong Hang and Animashree Anandkumar, white paper 2016. Download: PDFCodeVisualization.

    • "Escaping From Saddle Points – Online Stochastic Gradient for Tensor Decomposition", by R. Ge, F. Huang, C. Jin, Y. Yuan, COLT 2015. Download: PDF.

    • "

      Are you going to the party: depends, who else is coming? –Learning hidden group dynamics via conditional latent tree 

      models

      ", by 
      F. Arabshahi, F. Huang, A. Anandkumar, C. Butts ICDM 2015. Download: PDFProject Page.
    • "

      Discovering Neuronal Cell Types and Their Gene 

       

      Expression Profiles Using a Spatial Point Process 

       

      Mixture Model

       
      ", by 
      Furong Huang, Animashree Anandkumar, Christian Borgs, Jennifer Chayes, Ernest Fraenkel, Michael Hawrylycz, Ed Lein, Alessandro Ingrosso, Srinivas Turaga. Appeared at NIPS BigNeuro 2015: Making sense of big neural data workshop 2015. Download: Poster.

      • "Distributed Latent Dirichlet Allocation via Tensor Factorization", by F. Huang, S. Matusevych, A. Anandkumar, N. Karampatziakism and P. Mineiro, NIPS Optimization for Machine Learning workshop 2014. Download: PDF.

      • "FCD: Fast-Concurrent-Distributed Load Balancing under Switching Costs and Imperfect Observations", by F. Huang and A. Anandkumar. Accepted to the 32nd Annual IEEE International Conference on Computer Communications( INFOCOM'2013), Turin, Italy, Apr.2013.
      • "Learning High-Dimensional Mixtures of Graphical Models", by  A. Anandkumar, D. Hsu, F. Huang and S.M. Kakade. NIPS 2012. Download: PDF.
      • “High-Dimensional Structure Learning of Ising Models: Local Separation Criterion”, by A. Anandkumar, V.Y.F Tan, F. Huang, and A.S. Willsky. Accepted to Annals of Statistics, Feb. 2012. An abridged version appears in the Proc. of NIPS, Dec. 2011. Download: PDF.
      • "High-Dimensional Gaussian Graphical Model Selection:  Walk-Summability and Local Separation Criterion", by A. Anandkumar, V.Y.F Tan, F. Huang, and A.S. Willsky.  Accepted to J. Machine Learning Research, June 2012. An abridged version appears in the Proc. of NIPS, Dec. 2011. Download: PDF.

      • "Prediction-based Spectrum Aggregation with Hardware Limitation in Cognitive Radio Networks", by Furong Huang, Wei Wang, Zhaoyang Zhang. IEEE Vehicular Technology Conference, 2010-Spring.