• Composite Objective Optimization and Learning for Massive Datasets 

      Singer, Yoram (Georgia Institute of Technology, 2010-09-03)
      Composite objective optimization is concerned with the problem of minimizing a two-term objective function which consists of an empirical loss function and a regularization function. Application with massive datasets often ...
    • Graphical Models for the Internet 

      Smola, Alexander (Georgia Institute of Technology, 2011-04-29)
      In this talk I will present algorithms for performing large scale inference using Latent Dirichlet Allocation and a novel Cluster-Topic model to estimate user preferences and to group stories into coherent, topically ...
    • Modeling Rich Structured Data via Kernel Distribution Embeddings 

      Song, Le (Georgia Institute of Technology, 2011-03-25)
      Real world applications often produce a large volume of highly uncertain and complex data. Many of them have rich microscopic structures where each variable can take values on manifolds (e.g., camera rotations), combinatorial ...
    • Optimization for Machine Learning: SMO-MKL and Smoothing Strategies 

      Vishwanathan, S. V. N. (Georgia Institute of Technology, 2011-04-15)
      Our objective is to train $p$-norm Multiple Kernel Learning (MKL) and, more generally, linear MKL regularised by the Bregman divergence, using the Sequential Minimal Optimization (SMO) algorithm. The SMO algorithm is simple, ...