Jean-Philippe Pellet’s Personal Pages

Publications

• Automatic Extraction of Formal Features from Word, Excel, and PowerPoint Productions in a Diagnostic-Assessment Perspective.
  Jean-Philippe Pellet and Morgane Chevalier (2014), Proceedings of the International Conference on Education Technologies and Computers.
  
  
  
  Presentation at ICETC

• Predicting Individual Graduate-Level Performance from Undergraduate Achievements.
  Judith Zimmermann, Kay H. Brodersen, Jean-Philippe Pellet, Elias August, and Joachim M. Buhmann (2011), Proceedings of the 4th International Conference on Educational Data Mining.
  

• Effective Causal Analysis: Methods for Structure Learning and Explanations.
  Jean-Philippe Pellet (2010), Ph.D. Thesis, ETH Zurich & IBM Research.
  

• Development Projects for the Causality Workbench.
  Isabelle Guyon, Constantin Aliferis, Gregory Cooper, André Elisseeff, Jean-Philippe Pellet, Peter Spirtes, and Alexander Statnikov (2010), Proceedings of the AAAI Symposium on Artificial Intelligence for Development.
  

• Causal Networks for Risk and Compliance: Methodology and Application.
  André Elisseeff, Jean-Philippe Pellet, and Eleni Pratsini (2010), IBM Journal of Research and Development, Volume 54 (3).
  

• Causality Workbench.
  Isabelle Guyon, Constantin Aliferis, Gregory Cooper, André Elisseeff, Jean-Philippe Pellet, Peter Spirtes, and Alexander Statnikov (2010), in P. M. Illari, F. Russo, and J. Williamson (eds.), Causality in the Sciences.
  

• Design and Analysis of the Causality Pot-Luck Challenge.
  Isabelle Guyon, Constantin Aliferis, Gregory Cooper, André Elisseeff, Jean-Philippe Pellet, Peter Spirtes, and Alexander Statnikov (2009), Clopinet (Berkeley, California). Technical Report 4566.
  

• Design and Analysis of the Causation and Prediction Challenge.
  Isabelle Guyon, Constantin Aliferis, Gregory Cooper, André Elisseeff, Jean-Philippe Pellet, Peter Spirtes, and Alexander Statnikov (2008), JMLR Workshop and Conference Proceedings, Volume 3: Causation and Prediction Challenge (WCCI 2008), pp. 1–33.
  

• Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets.
  Jean-Philippe Pellet and André Elisseeff (2008), Proceedings of the 22nd Annual Conference on Neural Information Processing Systems.
  

• Using Markov Blankets for Causal Structure Learning.
  Jean-Philippe Pellet and André Elisseeff (2008), Journal of Machine Learning Research, Volume 9, pp. 1295–1342.
  

• Explanation Trees for Causal Bayesian Networks.
  Ulf Holm Nielsen, Jean-Philippe Pellet, and André Elisseeff (2008), in D. McAllester and P. Myllymäki (eds), Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence, AUAI Press, pp. 427–434.
  
  
  

  Presentation at UAI

  Presentation at GIF

• A Partial Correlation-Based Algorithm for Causal Structure Discovery with Continuous Variables.
  Jean-Philippe Pellet and André Elisseeff (2007), in M. R. Berthold, J. Shawe-Taylor and N. Lavrač (eds), Advances in Intelligent Data Analysis VII, 7th International Symposium on Intelligent Data Analysis, pp. 229–239.
  
  
  
  Presentation at IDA

• Partial Correlation- and Regression-Based Approaches to Causal Structure Learning.
  Jean-Philippe Pellet and André Elisseeff (2007), IBM Research. Technical Report.
  

• From Data to Causal Structure: Representation & Search.
  Jean-Philippe Pellet (2006), Master’s Thesis, Swiss Federal Institute of Technology, Lausanne (EPFL) & IBM Research.