NIPS Proceedingsβ

Francis Bach

22 Papers

  • Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization (2018)
  • On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport (2018)
  • Optimal Algorithms for Non-Smooth Distributed Optimization in Networks (2018)
  • Relating Leverage Scores and Density using Regularized Christoffel Functions (2018)
  • Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes (2018)
  • SING: Symbol-to-Instrument Neural Generator (2018)
  • Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes (2018)
  • Integration Methods and Optimization Algorithms (2017)
  • Nonlinear Acceleration of Stochastic Algorithms (2017)
  • On Structured Prediction Theory with Calibrated Convex Surrogate Losses (2017)
  • PAC-Bayesian Theory Meets Bayesian Inference (2016)
  • Parameter Learning for Log-supermodular Distributions (2016)
  • Regularized Nonlinear Acceleration (2016)
  • Stochastic Optimization for Large-scale Optimal Transport (2016)
  • Stochastic Variance Reduction Methods for Saddle-Point Problems (2016)
  • Rethinking LDA: Moment Matching for Discrete ICA (2015)
  • Spectral Norm Regularization of Orthonormal Representations for Graph Transduction (2015)
  • Metric Learning for Temporal Sequence Alignment (2014)
  • SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives (2014)
  • Convex Relaxations for Permutation Problems (2013)
  • Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) (2013)
  • Reflection methods for user-friendly submodular optimization (2013)