NIPS 2012 Workshop on

Bayesian Optimization & Decision Making




December 7th 2012, Lake Tahoe, Nevada, USA

  Home Submissions Program Committee Schedule  
 Morning Session: 7:30-10:30 AM
  • 7:30-7:50 Introduction.
  • 7:50-8:30 Poster spotlights.
  • 8:30-9:00 Poster session.
  • 9:00-9:30 Coffee Break (same for all workshops).
  • 9:30-10:05 Invited talk 1: Holger Hoos.
  • 10:05-10:30 Poster session.
  • 10:30-15:30 Break (same for all workshops).
 Afternoon Session: 15:30-18:30 AM
  • 15:30-16:05 Invited talk 2: Carlos Guestrin.
  • 16:05-16:40 Invited talk 3: James Bergstra.
  • 16:40-17:00 Poster session.
  • 17:00-17:30 Coffee Break (same for all workshops).
  • 17:30-18:30 Industrial Panel.
 Accepted Papers
  • Roman Marchant and Fabio Ramos.
  • Bayesian Optimisation for Intelligent Environmental Monitoring. [.pdf]
  • Remi Bardenet, Mátyás Brendel, Balazs Kegl and Michele Sebag.
  • SCOT: surrogate-based collaborative tuning for hyperparameter learning that remembers the past. [.pdf]
  • R. Girdziuas, J. Janusevskis, and R. Le Riche.
  • On Integration of Multi-Point Improvements [.pdf]
  • R. Le Riche and R. Girdziuaas, J. Janusevskis.
  • A Study of Asynchronous Budgeted Optimization. [.pdf]
  • Xuezhi Wang, Roman Garnett, and Jeff Schneider.
  • An Impact Criterion for Active Graph Search.[.pdf]
  • Clement Chevalier and David Ginsbourger
  • Fast computation of the multipoint Expected Improvement with applications in batch selection.[.pdf]
  • Ali Jalali, Javad Azimi and Xiaoli Fern.
  • Exploration vs Exploitation in Bayesian Optimization.[.pdf]
  • Nadjib Lazaar, Youssef Hamadi, Said Jabbour, and Michele Sebag.
  • Cooperation control in Parallel SAT Solving: a Multi-armed Bandit Approach. [.pdf]
  • Christopher Amato, and Emma Brunskill.
  • Diagnose and Decide: An Optimal Bayesian Approach. [.pdf]
  • Matthew Tesch, Jeff Schneider and Howie Choset.
  • Expensive Multiobjective Optimization and Validation with a Robotics Application.[.pdf]
  • Zheng Wen, Branislav Kveton, and Sandilya Bhamidipati
  • Learning to Discover: A Bayesian Approach. [.pdf]