Talks & Conference Presentations

  • Optimal Sparse Designs for Process Flexibility via Probabilistic Expanders, 06/2014
    MSOM Conference, Seattle, WA.
  • Statistical Decision Making for Optimal Budget Allocation in Crowdsourcing, 03/2014
    Google Research at Mountain View, CA.
  • Optimal Sparse Designs for Process Flexibility via Probabilistic Expanders, 02/2014
    Department of Industrial Engineering and Operations Research, UC Berkeley, CA.
  • Optimal Sparse Designs for Process Flexibility via Probabilistic Expanders, 02/2014
    Fuqua School of Business, Duke University, NC.
  • Statistical Decision Making for Optimal Budget Allocation in Crowdsourcing, 01/2014
    Data Science Seminar, Stanford University, CA.
  • Statistical Decision Making for Optimal Budget Allocation in Crowdsourcing, 10/2013
    Informs Annual Meeting, MN.
  • Statistical Decision Making for Optimal Budget Allocation in Crowdsourcing, 04/2013
    Microsoft Research at New York, NY.
  • Uniformly-optimal Stochastic Dual Averaging Methods with Double Projections, 10/2012
    Informs Annual Meeting, AZ.
  • Optimal Budget Allocation in Crowdsourcing, 10/2012
    Microsoft Research Redmond, WA.
  • Predicting Consensus Ranking in Crowdsourced Setting, 07/2012
    Microsoft Research Redmond, WA.
  • Structured Sparse Canonical Correlation Analysis, 04/2012
    International Conference on Artificial Intelligence and Statistics (AISTATS).
  • Optimization for General Structured Sparse Learning, 03/2012
    Microsoft Research Redmond, WA.
  • Sparse Learning for Text Mining, 12/2011
    Microsoft Research Aisa, Beijing, China.
  • Graph-Valued Regression, 08/2011
    Rutgers Business School, The State University of NJ, NJ.
  • Graph-Valued Regression, 08/2011
    NEC Lab American, NJ.
  • Proximal Gradient Descent for Structured Sparse Learning, 08/2010
    Department of Computer Science, Princeton University, NJ.
  • Multivariate Dyadic Regression Trees for Sparse Learning Problems, 08/2010
    Joint Statistical Meeting 2010, Vancouver, Canada.
  • Proximal Gradient Descent for Structured Sparse Learning, 07/2010
    Department of Computer Science, University of British Columbia, Vancouver, Canada.
  • Proximal Gradient Descent for Structured Sparse Learning, 07/2010
    IBM Thomas J. Watson Research Center, NY.
  • Accelerated Gradient Method for Multi-Task Sparse Learning Problem 12/2009
    • International Conference on Data Mining (ICDM).


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