NIPS Proceedingsβ

Simon Lacoste-Julien

11 Papers

  • Quantifying Learning Guarantees for Convex but Inconsistent Surrogates (2018)
  • Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization (2017)
  • On Structured Prediction Theory with Calibrated Convex Surrogate Losses (2017)
  • PAC-Bayesian Theory Meets Bayesian Inference (2016)
  • Barrier Frank-Wolfe for Marginal Inference (2015)
  • On the Global Linear Convergence of Frank-Wolfe Optimization Variants (2015)
  • Rethinking LDA: Moment Matching for Discrete ICA (2015)
  • Variance Reduced Stochastic Gradient Descent with Neighbors (2015)
  • SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives (2014)
  • DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification (2008)
  • Structured Prediction via the Extragradient Method (2005)