Copertina

Toward Exact Convergence in Byzantine-Robust Decentralized Learning: A Statistical Identification Approach

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Siyuan, Qian, Chengde, Liu, Xin, Zou, Changliang
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Methodology Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning
di: Li, Michael Lingzhi, et al.
Pubblicazione: (2023)

PNOD: An Efficient Projected Newton Framework for Exact Optimal Experimental Designs
di: Liang, Ling, et al.
Pubblicazione: (2024)

Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions
di: Zhang, Siyuan, et al.
Pubblicazione: (2024)

Robust Multi-Source Domain Adaptation under Label Shift
di: Xu, Congbin, et al.
Pubblicazione: (2025)

A Graph-Prediction-Based Approach for Debiasing Underreported Data
di: Jiang, Hanyang, et al.
Pubblicazione: (2023)

Reliever: Relieving the Burden of Costly Model Fits for Changepoint Detection
di: Qian, Chengde, et al.
Pubblicazione: (2023)

SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
di: Islam, H M Mohaimanul, et al.
Pubblicazione: (2024)

DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
di: Dong, Shuyu, et al.
Pubblicazione: (2024)

Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
di: Sordello, Matteo, et al.
Pubblicazione: (2019)

Filtering Dynamical Systems Using Observations of Statistics
di: Bach, Eviatar, et al.
Pubblicazione: (2023)

Statistical Analysis of Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss
di: Guo, Zijian, et al.
Pubblicazione: (2025)

Posterior and Likelihood Sensitivity in Bayesian Distributionally Robust Optimization
di: Gotoh, Jun-ya, et al.
Pubblicazione: (2026)

Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processes
di: Xu, Mingyuan, et al.
Pubblicazione: (2026)

Learning Interactions Between Continuous Treatments and Covariates with a Semiparametric Model
di: Jiang, Muyan, et al.
Pubblicazione: (2025)

Fenchel-Young Estimators of Perturbed Utility Models
di: Lin, Xi, et al.
Pubblicazione: (2026)

StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data
di: Wang, Zhenyu, et al.
Pubblicazione: (2025)

Shape-Constrained Distributional Optimization via Importance-Weighted Sample Average Approximation
di: Lam, Henry, et al.
Pubblicazione: (2024)

Joint optimization for production operations considering reworking
di: Shen, Yilan, et al.
Pubblicazione: (2024)

Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting
di: Garcia, Joaquim Dias, et al.
Pubblicazione: (2021)

Convergence of projected stochastic natural gradient variational inference for various step size and sample or batch size schedules
di: Guilmeau, Thomas, et al.
Pubblicazione: (2026)

Robust Data-driven Prescriptiveness Optimization
di: Poursoltani, Mehran, et al.
Pubblicazione: (2023)

Derivative Estimation from Coarse, Irregular, Noisy Samples: An MLE-Spline Approach
di: Avrachenkov, Konstantin E., et al.
Pubblicazione: (2025)

Truncated Multidimensional Trigonometric Moment Problem: A Choice of Bases and the Unique Solution
di: Wu, Guangyu, et al.
Pubblicazione: (2026)

A Non-Gaussian Bayesian Filter Using Power and Generalized Logarithmic Moments
di: Wu, Guangyu, et al.
Pubblicazione: (2022)

Byzantine-Resilient Decentralized Online Resource Allocation
di: Wang, Runhua, et al.
Pubblicazione: (2025)

Changepoint Detection in Complex Models: Cross-Fitting Is Needed
di: Qian, Chengde, et al.
Pubblicazione: (2024)

Fast Robust Kernel Regression through Sign Gradient Descent with Early Stopping
di: Allerbo, Oskar
Pubblicazione: (2023)

Batch-based Bayesian Optimal Experimental Design in Linear Inverse Problems
di: Mäkinen, Sofia, et al.
Pubblicazione: (2026)

Unmixing highly mixed grain size distribution data via maximum volume constrained end member analysis
di: Qi, Qianqian, et al.
Pubblicazione: (2026)

Solving Minimax Problems with Bilinear Objectives with ADMM
di: Wilson, Bob
Pubblicazione: (2026)

Feasibility Determination for Subjective Probability Constraints
di: Kim, Taehoon, et al.
Pubblicazione: (2026)

An Efficient Dual ADMM for Huber Regression with Fused Lasso Penalty
di: Shi, Mengjiao, et al.
Pubblicazione: (2025)

The Monge optimal transport barycenter problem
di: Lipnick, Andrew D., et al.
Pubblicazione: (2025)

Sensitivity Analysis for Binary Outcome Misclassification in Randomization Tests via Integer Programming
di: Heng, Siyu, et al.
Pubblicazione: (2022)

Tuning parameter selection for the adaptive nuclear norm regularized trace regression
di: Shang, Pan, et al.
Pubblicazione: (2024)

Clustering sequence data with mixture Markov chains with covariates using multiple simplex constrained optimization routine (MSiCOR)
di: Das, Priyam, et al.
Pubblicazione: (2016)

Constrained Markov decision processes for response-adaptive procedures in clinical trials with binary outcomes
di: Baas, Stef, et al.
Pubblicazione: (2024)

Fixed Confidence and Fixed Tolerance Bi-level Optimization for Selecting the Best Optimized System
di: Wang, Yuhao, et al.
Pubblicazione: (2025)

Distributionally robust halfspace depth
di: Ivanovs, Jevgenijs, et al.
Pubblicazione: (2021)

Distributed Online Economic Dispatch with Time-Varying Coupled Inequality Constraints
di: Zhou, Yingjie, et al.
Pubblicazione: (2025)