strong convexity

Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms ICML 2017 Improved Dynamic Regret for Non-degenerate Functions NeurIPS 2017 Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization NeurIPS 2017 DSA: Decentralized Double Stochastic Averaging Gradient Algorithm JMLR 2016 Optimal Black-Box Reductions Between Optimization Objectives NeurIPS 2016 SDCA without Duality, Regularization, and Individual Convexity ICML 2016 A General Analysis of the Convergence of ADMM ICML 2015 Smooth and Strong: MAP Inference with Linear Convergence NeurIPS 2015 Adaptivity of Averaged Stochastic Gradient Descent to Local Strong Convexity for Logistic Regression JMLR 2014 A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights NeurIPS 2014 An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization ICML 2014 Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings NeurIPS 2014 SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives NeurIPS 2014 Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm NeurIPS 2014 Linear Convergence with Condition Number Independent Access of Full Gradients NeurIPS 2013 Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions NeurIPS 2012 Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization NeurIPS 2011 Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity AISTATS 2010 Fast Rates for Regularized Objectives NeurIPS 2008 Mind the Duality Gap: Logarithmic regret algorithms for online optimization NeurIPS 2008 On the Generalization Ability of Online Strongly Convex Programming Algorithms NeurIPS 2008