NIPS Proceedings
^{β}
Books
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)