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convex optimization

1320 papers

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Also known as

ACO ADMM CO OCO

Co-occurring keywords

stochastic optimization (1060) convergence rate (606) stochastic gradient descent (1088) gradient descent (1143) online learning (1770) support vector machine (976) matrix completion (356) regret bound (1918) kernel methods (1096) sparse optimization (246)

Papers

Heavy Ball Momentum for Conditional Gradient NIPS 2021
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems NIPS 2021
Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality ICML 2021
Optimized auxiliary particle filters: adapting mixture proposals via convex optimization UAI 2021
Certifying Incremental Quadratic Constraints for Neural Networks via Convex Optimization L4DC 2021
Private Adaptive Gradient Methods for Convex Optimization ICML 2021
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression NIPS 2021
Learning with semi-definite programming: statistical bounds based on fixed point analysis and excess risk curvature JMLR 2021
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them JMLR 2021
Localization, Convexity, and Star Aggregation NIPS 2021
The ensmallen library for flexible numerical optimization JMLR 2021
How Data Augmentation affects Optimization for Linear Regression NIPS 2021
Faster proximal algorithms for matrix optimization using Jacobi-based eigenvalue methods NIPS 2021
Remember What You Want to Forget: Algorithms for Machine Unlearning NIPS 2021
GemBag: Group Estimation of Multiple Bayesian Graphical Models JMLR 2021
Information-constrained optimization: can adaptive processing of gradients help? NIPS 2021
A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints JMLR 2021
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning NIPS 2021
A first-order primal-dual method with adaptivity to local smoothness NIPS 2021
Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence NIPS 2021
Rethinking the Variational Interpretation of Accelerated Optimization Methods NIPS 2021
Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions L4DC 2021
Learning to Learn Graph Topologies NIPS 2021
Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates NIPS 2021
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks NIPS 2021