One scholarship of 555 € will be awarded to a student author (please mark student submissions on the title page) of an excellent paper.

The E. M. Gold Award Winner for ALT 2012 is:

for the paper

co-authored by Frank Stephan

Previous E.M. Gold Award Winners

• ALT 2011: Malte Darnstädt for his paper

  “Supervised Learning and Co-training”
  
  co-authored by 
  Hans Ulrich Simon and Balázs Szörényi.
  

• ALT 2010: Gábor Bartók for his paper

    “Toward a Classification of Finite Partial-Monitoring Games”
  
  co-authored by Dávid Pál and Csaba Szepesvári.
  

• ALT 2009: Hanna Mazzawi for his paper

    “Reconstructing Weighted Graphs with Minimal Query Complexity”
         co-authored by Nader Bshouty
  

• ALT 2008: Mikhail Dashevskiy for his paper

    “Aggregating Algorithm for a Space of Analytic Functions”
  

• ALT 2007: Markus Maier for his paper

    “Cluster Identification in Nearest-Neighbor Graphs”
           
         co-authored by Matthias Hein and Ulrike von Luxburg
  

• ALT 2006: Alp Atıcı for his paper

  “Learning Unions of ω(1)-Dimensional Rectangles”
         co-authored by Rocco A. Servedio      

• ALT 2005: Rotem Bennet for his paper

      “Learning Attribute-Efficiently with Corrupt Oracles”
         co-authored by Nader Bshouty
  

• ALT 2004: Hubie Chen for his paper

      “Learnability of Relatively Quantified Generalized Formulas.”
        co-authored by Andrei Bulatov and Víctor Dalmau
  

• ALT 2003: Sandra Zilles for her paper

      “Intrinsic Complexity of Uniform Learning.”
  

• ALT 2002: Daniel Reidenbach for his paper

      “A Negative Result on Inductive Inference of Extended Pattern Languages”
  

• ALT 2001: Ke Yang for his paper

      “On Learning Correlated Boolean Functions Using Statistical Queries.”
  

• ALT 2000: Gunter Grieser for the paper

      “Learning of recursive concepts with anomalies.”
        co-authored by Steffen Lange and Thomas Zeugmann
  

• ALT 1999: Yuri Kalnishkan for his paper

      “General Linear Relations among Different Types of Predictive Complexity.”