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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

Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization ICML 2020
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks ICML 2020
The Wasserstein Proximal Gradient Algorithm NIPS 2020
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent ICML 2020
Provable Convex Co-clustering of Tensors JMLR 2020
Recovery of a Mixture of Gaussians by Sum-of-Norms Clustering JMLR 2020
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach AISTATS 2020
Memoryless Sequences for General Losses JMLR 2020
Theory of Curriculum Learning, with Convex Loss Functions JMLR 2020
Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels JMLR 2020
First-Order Methods for Large-Scale Market Equilibrium Computation NIPS 2020
Distributionally Ambiguous Optimization for Batch Bayesian Optimization JMLR 2020
Minibatch Stochastic Approximate Proximal Point Methods NIPS 2020
Discerning the Linear Convergence of ADMM for Structured Convex Optimization through the Lens of Variational Analysis JMLR 2020
Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training AISTATS 2020
Learning with Fenchel-Young losses JMLR 2020
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent JMLR 2020
On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent JMLR 2020
Convex Geometry of Two-Layer ReLU Networks: Implicit Autoencoding and Interpretable Models AISTATS 2020
Lower Bounds for Parallel and Randomized Convex Optimization JMLR 2020
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling AISTATS 2020
EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices UAI 2020
Sharp Asymptotics and Optimal Performance for Inference in Binary Models AISTATS 2020
Stochastic Subspace Cubic Newton Method ICML 2020
Robust Policy Synthesis for Uncertain POMDPs via Convex Optimization IJCAI 2020