Image de couverture de livre

Exact Instance Compression for Convex Empirical Risk Minimization via Color Refinement

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zhu, Bryan, Chen, Ziang
Format:	Preprint
Publié:	2026
Sujets:	Optimization and Control Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

A Theoretical and Empirical Study on the Convergence of Adam with an "Exact" Constant Step Size in Non-Convex Settings
par: Mazumder, Alokendu, et autres
Publié: (2023)

Specifying and Solving Robust Empirical Risk Minimization Problems Using CVXPY
par: Luxenberg, Eric, et autres
Publié: (2023)

Using Taylor-Approximated Gradients to Improve the Frank-Wolfe Method for Empirical Risk Minimization
par: Xiong, Zikai, et autres
Publié: (2022)

Empirical Risk Minimization with Shuffled SGD: A Primal-Dual Perspective and Improved Bounds
par: Cai, Xufeng, et autres
Publié: (2023)

A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
par: Dagréou, Mathieu, et autres
Publié: (2023)

Muon Converges under Heavy-Tailed Noise: Nonconvex Hölder-Smooth Empirical Risk Minimization
par: Iiduka, Hideaki
Publié: (2026)

Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Data
par: Qiu, Pu, et autres
Publié: (2026)

Convergence Analysis of the Wasserstein Proximal Algorithm beyond Geodesic Convexity
par: Zhu, Shuailong, et autres
Publié: (2025)

Lower Bounds and Proximally Anchored SGD for Non-Convex Minimization Under Unbounded Variance
par: Fazla, Arda, et autres
Publié: (2026)

Escaping Saddle Points for Nonsmooth Weakly Convex Functions via Perturbed Proximal Algorithms
par: Huang, Minhui, et autres
Publié: (2021)

Clipped Gradient Methods for Nonsmooth Convex Optimization under Heavy-Tailed Noise: A Refined Analysis
par: Liu, Zijian
Publié: (2025)

A Randomized Linearly Convergent Frank-Wolfe-type Method for Smooth Convex Minimization over the Spectrahedron
par: Garber, Dan
Publié: (2025)

SOREL: A Stochastic Algorithm for Spectral Risks Minimization
par: Ge, Yuze, et autres
Publié: (2024)

Are Convex Optimization Curves Convex?
par: Barzilai, Guy, et autres
Publié: (2025)

Parametric Nonconvex Optimization via Convex Surrogates
par: Wang, Renzi, et autres
Publié: (2026)

Online (Non-)Convex Learning via Tempered Optimism
par: Haddouche, Maxime, et autres
Publié: (2023)

Stochastic Weakly Convex Optimization Under Heavy-Tailed Noises
par: Zhu, Tianxi, et autres
Publié: (2025)

Solving Sparse \& High-Dimensional-Output Regression via Compression
par: Li, Renyuan, et autres
Publié: (2024)

Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
par: Chen, Ziang, et autres
Publié: (2024)

Rethinking the Capacity of Graph Neural Networks for Branching Strategy
par: Chen, Ziang, et autres
Publié: (2024)

RedEx: Beyond Fixed Representation Methods via Convex Optimization
par: Daniely, Amit, et autres
Publié: (2024)

An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling
par: Yang, Linxin, et autres
Publié: (2024)

Global Optimization via Softmin Energy Minimization
par: Agazzi, Andrea, et autres
Publié: (2025)

Bandit Convex Optimisation
par: Lattimore, Tor
Publié: (2024)

Damped Proximal Augmented Lagrangian Method for weakly-Convex Problems with Convex Constraints
par: Dahal, Hari, et autres
Publié: (2023)

Optimistic Safety for Online Convex Optimization with Unknown Linear Constraints
par: Hutchinson, Spencer, et autres
Publié: (2024)

Inclusive KL Minimization: A Wasserstein-Fisher-Rao Gradient Flow Perspective
par: Zhu, Jia-Jie
Publié: (2024)

Functionally Constrained Algorithm Solves Convex Simple Bilevel Problems
par: Zhang, Huaqing, et autres
Publié: (2024)

Scalable Min-Max Optimization via Primal-Dual Exact Pareto Optimization
par: Park, Sangwoo, et autres
Publié: (2025)

Achieving Better Local Regret Bound for Online Non-Convex Bilevel Optimization
par: Jia, Tingkai, et autres
Publié: (2026)

Natural Geometry of Robust Data Attribution: From Convex Models to Deep Networks
par: Li, Shihao, et autres
Publié: (2025)

Sharpness-Aware Minimization Can Hallucinate Minimizers
par: Park, Chanwoong, et autres
Publié: (2025)

Adversarial Training of Two-Layer Polynomial and ReLU Activation Networks via Convex Optimization
par: Kuelbs, Daniel, et autres
Publié: (2024)

Stochastic Difference-of-Convex Optimization with Momentum
par: Chayti, El Mahdi, et autres
Publié: (2025)

The Price of Adaptivity in Stochastic Convex Optimization
par: Carmon, Yair, et autres
Publié: (2024)

Online Convex Optimization with Unbounded Memory
par: Kumar, Raunak, et autres
Publié: (2022)

Astral Space: Convex Analysis at Infinity
par: Dudík, Miroslav, et autres
Publié: (2022)

Smooth Quasar-Convex Optimization with Constraints
par: Martínez-Rubio, David
Publié: (2025)

Unconstrained Robust Online Convex Optimization
par: Zhang, Jiujia, et autres
Publié: (2025)

A Distance Metric for Mixed Integer Programming Instances
par: Maudet, Gwen, et autres
Publié: (2025)