conftrace_

strong convexity

59 papers

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Co-occurring keywords

convex optimization (1321) linear convergence (158) strongly convex (89) stochastic optimization (1060) stochastic gradient descent (1091) convergence rate (607) gradient descent (1144) regret bound (1926) stochastic gradient (296) stochastic convex optimization (69)

Papers

A Study of Condition Numbers for First-Order Optimization AISTATS 2021
Revisiting Projection-free Online Learning: the Strongly Convex Case AISTATS 2021
From Low Probability to High Confidence in Stochastic Convex Optimization JMLR 2021
Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport AISTATS 2020
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates NIPS 2020
Learning Interpretable Models using Soft Integrity Constraints ACML 2020
Linearly Convergent Frank-Wolfe with Backtracking Line-Search AISTATS 2020
Stochastic Optimization for Performative Prediction NIPS 2020
Random Reshuffling: Simple Analysis with Vast Improvements NIPS 2020
Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the $O(1/T)$ Convergence Rate COLT 2019
Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity AISTATS 2019
Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions 2: Numerical integrators AISTATS 2019
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods ICML 2018
Batch-Expansion Training: An Efficient Optimization Framework AISTATS 2018
Convergence of Langevin MCMC in KL-divergence ALT 2018
Improved Dynamic Regret for Non-degenerate Functions NIPS 2017
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter NIPS 2017
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms ICML 2017
Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds COLT 2017
Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization NIPS 2017
Optimal Black-Box Reductions Between Optimization Objectives NIPS 2016
SDCA without Duality, Regularization, and Individual Convexity ICML 2016
DSA: Decentralized Double Stochastic Averaging Gradient Algorithm JMLR 2016
Smooth and Strong: MAP Inference with Linear Convergence NIPS 2015
A General Analysis of the Convergence of ADMM ICML 2015