NIPS Proceedingsβ

Dale Schuurmans

21 Papers

  • Deep Learning Games (2016)
  • Reward Augmented Maximum Likelihood for Neural Structured Prediction (2016)
  • Embedding Inference for Structured Multilabel Prediction (2015)
  • Convex Deep Learning via Normalized Kernels (2014)
  • Convex Two-Layer Modeling (2013)
  • Polar Operators for Structured Sparse Estimation (2013)
  • Accelerated Training for Matrix-norm Regularization: A Boosting Approach (2012)
  • A Polynomial-time Form of Robust Regression (2012)
  • Convex Multi-view Subspace Learning (2012)
  • Relaxed Clipping: A Global Training Method for Robust Regression and Classification (2010)
  • A General Projection Property for Distribution Families (2009)
  • Convex Relaxation of Mixture Regression with Efficient Algorithms (2009)
  • Convex Relaxations of Latent Variable Training (2007)
  • Discriminative Batch Mode Active Learning (2007)
  • Stable Dual Dynamic Programming (2007)
  • implicit Online Learning with Kernels (2006)
  • Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields (2006)
  • Maximum Margin Clustering (2004)
  • Regularized Greedy Importance Sampling (2002)
  • Direct value-approximation for factored MDPs (2001)
  • Greedy Importance Sampling (1999)
  • 2 Books

  • Advances in Neural Information Processing Systems 22 (2009)
  • Advances in Neural Information Processing Systems 21 (2008)