"non-convex optimization" Papers

Attention layers provably solve single-location regression

Pierre Marion, Raphaël Berthier, Gérard Biau et al.

Balancing Gradient and Hessian Queries in Non-Convex Optimization

Deeksha Adil, Brian Bullins, Aaron Sidford et al.

Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates

Andrew Lowy, Daogao Liu

Efficient Adaptive Federated Optimization

Su Hyeong Lee, Sidharth Sharma, Manzil Zaheer et al.

Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients

Shiyuan Zuo, Xingrun Yan, Rongfei Fan et al.

Faster Stochastic Optimization with Arbitrary Delays via Adaptive Asynchronous Mini-Batching

Amit Attia, Ofir Gaash, Tomer Koren

Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence

Shayan Talaei, Matin Ansaripour, Giorgi Nadiradze et al.

Linear Mode Connectivity in Differentiable Tree Ensembles

Ryuichi Kanoh, Mahito Sugiyama

Machine Unlearning via Simulated Oracle Matching

Kristian G Georgiev, Roy Rinberg, Sam Park et al.

Memory-Augmented Potential Field Theory: A Framework for Adaptive Control in Non-Convex Domains

Dongzhe Zheng, Wenjie Mei

Nesterov acceleration in benignly non-convex landscapes

Kanan Gupta, Stephan Wojtowytsch

Non-convex entropic mean-field optimization via Best Response flow

Razvan-Andrei Lascu, Mateusz Majka

Non-Convex Tensor Recovery from Tube-Wise Sensing

Tongle Wu, Ying Sun

Quantum Speedups for Minimax Optimization and Beyond

Chengchang Liu, Zongqi Wan, Institute of Computing Jialin Zhang et al.

Revisiting Large-Scale Non-convex Distributionally Robust Optimization

Qi Zhang, Yi Zhou, Simon Khan et al.

Sharpness-Aware Minimization: General Analysis and Improved Rates

Dimitris Oikonomou, Nicolas Loizou

Spike-timing-dependent Hebbian learning as noisy gradient descent

Niklas Dexheimer, Sascha Gaudlitz, Johannes Schmidt-Hieber

Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression

Jiarui Jiang, Wei Huang, Miao Zhang et al.

Unlocking Global Optimality in Bilevel Optimization: A Pilot Study

Quan Xiao, Tianyi Chen

Unveiling the Power of Multiple Gossip Steps: A Stability-Based Generalization Analysis in Decentralized Training

A Universal Class of Sharpness-Aware Minimization Algorithms

Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri et al.

Barrier Algorithms for Constrained Non-Convex Optimization

Pavel Dvurechenskii, Mathias Staudigl

Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates

Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui et al.

Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics

Haoyang Zheng, Hengrong Du, Qi Feng et al.

Convex Relaxations of ReLU Neural Networks Approximate Global Optima in Polynomial Time

Sungyoon Kim, Mert Pilanci

Fair Federated Learning via the Proportional Veto Core

Bhaskar Ray Chaudhury, Aniket Murhekar, Zhuowen Yuan et al.

Generalization Analysis of Stochastic Weight Averaging with General Sampling

Wang Peng, Li Shen, Zerui Tao et al.

Global Optimality for Non-linear Constrained Restoration Problems via Invexity

Samuel Pinilla, Jeyan Thiyagalingam

High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails

Langqi Liu, Yibo Wang, Lijun Zhang

How Free is Parameter-Free Stochastic Optimization?

Amit Attia, Tomer Koren

How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization

Andrew Lowy, Jonathan Ullman, Stephen Wright

Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm

Batiste Le Bars, Aurélien Bellet, Marc Tommasi et al.

Improving Computational Complexity in Statistical Models with Local Curvature Information

Pedram Akbarian, Tongzheng Ren, Jiacheng Zhuo et al.

Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization

Qi Zhang, Yi Zhou, Ashley Prater-Bennette et al.

Monotone, Bi-Lipschitz, and Polyak-Łojasiewicz Networks

Ruigang Wang, Krishnamurthy Dvijotham, Ian Manchester

Non-convex Stochastic Composite Optimization with Polyak Momentum

Yuan Gao, Anton Rodomanov, Sebastian Stich

On a Neural Implementation of Brenier's Polar Factorization

Nina Vesseron, Marco Cuturi

On Convergence of Incremental Gradient for Non-convex Smooth Functions

Anastasiia Koloskova, Nikita Doikov, Sebastian Stich et al.

Random Scaling and Momentum for Non-smooth Non-convex Optimization

Qinzi Zhang, Ashok Cutkosky

Sample-and-Bound for Non-convex Optimization

Yaoguang Zhai, Zhizhen Qin, Sicun Gao

Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features

Aleksandr Beznosikov, David Dobre, Gauthier Gidel

SILVER: Single-loop variance reduction and application to federated learning

Kazusato Oko, Shunta Akiyama, Denny Wu et al.

Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions

Nikita Doikov, Sebastian Stich, Martin Jaggi

Stochastic Optimization with Arbitrary Recurrent Data Sampling

William Powell, Hanbaek Lyu

Supervised Matrix Factorization: Local Landscape Analysis and Applications

Joowon Lee, Hanbaek Lyu, Weixin Yao

Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data

Haitong LIU, Qiang Li, Hoi To Wai

What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis

Waïss Azizian, Franck Iutzeler, Jérôme Malick et al.