MISC:

• Model-based Machine Learning
  Christopher M.Bishop

• Tensor Decomposition Algorithms for Latent Variable Model Estimation
  Prof. Anima Anandkumar, Dr. Daniel Hsu, Dr. Sham M. Kakade

• Mixed Membership Models for Time Series
  Emily B. Fox, Michael I. Jordan

• Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data
  Thomas A. Lasko, Joshua C. Denny, Mia A. Levy

• Bayesian nonparametrics and the probabilistic approach to modelling
  Zoubin Ghahramani

• Detecting the Direction of Causal Time Series
  Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Scholkopf

ICML 2013 Papers:

• Modeling Temporal Evolution and Multiscale Structure in Networks
  Tue Herlau*, Technical University of Denmark; Morten Mørup, Technical University of Denmark; Mikkel Schmidt, Technical University of Denmark

• Learning the Structure of Sum-Product Networks
  Robert Gens*, University of Washington; Domingos Pedro, University of Washington

• Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures,
  James Bergstra*, Harvard University; Daniel Yamins; David Cox, Harvard University

• Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment
  Jason Chuang*, Stanford University; Sonal Gupta, Stanford University; Christopher Manning, Stanford University; Jeffrey Heer, Stanford University

• Parsing epileptic events using a Markov switching process model for correlated time series
  Drausin Wulsin*, University of Pennsylvania; Emily Fox, University of Washington; Brian Litt, University of Pennsylvania

• Gaussian Process Kernels for Pattern Discovery and Extrapolation
  Andrew Wilson*, University of Cambridge; Ryan Adams

• Dynamic Covariance Models for Multivariate Financial Time Series
  Yue Wu*, Cambridge University; Jose Miguel Hernandez-Lobato, Cambridge University; Ghahramani Zoubin, Cambridge University

• Transition Matrix Estimation in High Dimensional Time Series
  Fang Han*, Johns Hopkins University; Han Liu, Princeton University

• Topic Discovery through Data Dependent and Random Projections
  Weicong Ding*, Boston University; Mohammad Hossein Rohban, Boston University; Prakash Ishwar, Boston University; Venkatesh Saligrama

• Forecastable Component Analysis
  Georg Goerg*, Carnegie Mellon University

• Spectral Learning of Hidden Markov Models from Dynamic and Static Data
  Tzu-Kuo Huang*, Carnegie Mellon University; Jeff Schneider

• Structure Discovery in Nonparametric Regression through Compositional Kernel Search
  David Duvenaud, University of Cambridge; James Lloyd*, University of Cambridge; Roger Grosse, Massachusetts Institute of Technology; Joshua Tenenbaum, Massachusetts Institute of Technology; Ghahramani Zoubin, Cambridge University

• Consistency of Online Random Forests
  Misha Denil*, University of British Columbia; David Matheson, University of British Columbia; De Freitas Nando, University of British Columbia

• A Local Algorithm for Finding Well-Connected Clusters
  Silvio Lattanzi, Google Research; Vahab Mirrokni, Google Research; Zeyuan Allen Zhu*, MIT CSAIL

• One-Pass AUC Optimization,
  Wei Gao*, Nanjing University; Rong Jin, Michigan State University; Shenghuo Zhu; Zhi-Hua Zhou, Nanjing University