Federated ADMM from Bayesian Duality
Fuente:
arXiv
Saved in:
| Main Authors: | Möllenhoff, Thomas, Swaroop, Siddharth, Doshi-Velez, Finale, Khan, Mohammad Emtiyaz |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Connecting Federated ADMM to Bayes
by: Swaroop, Siddharth, et al.
Published: (2025)
by: Swaroop, Siddharth, et al.
Published: (2025)
Federated Learning Using Three-Operator ADMM
by: Kant, Shashi, et al.
Published: (2022)
by: Kant, Shashi, et al.
Published: (2022)
Variational Learning is Effective for Large Deep Networks
by: Shen, Yuesong, et al.
Published: (2024)
by: Shen, Yuesong, et al.
Published: (2024)
Distributed Event-Based Learning via ADMM
by: Er, Guner Dilsad, et al.
Published: (2024)
by: Er, Guner Dilsad, et al.
Published: (2024)
Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
by: Lin, Junan, et al.
Published: (2026)
by: Lin, Junan, et al.
Published: (2026)
Learning to accelerate distributed ADMM using graph neural networks
by: Doerks, Henri, et al.
Published: (2025)
by: Doerks, Henri, et al.
Published: (2025)
HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
by: Tran, Trinh, et al.
Published: (2026)
by: Tran, Trinh, et al.
Published: (2026)
CoCoA Is ADMM: Unifying Two Paradigms in Distributed Optimization
by: Wu, Runxiong, et al.
Published: (2025)
by: Wu, Runxiong, et al.
Published: (2025)
A General Continuous-Time Formulation of Stochastic ADMM and Its Variants
by: Li, Chris Junchi
Published: (2024)
by: Li, Chris Junchi
Published: (2024)
ADMM Algorithms for Residual Network Training: Convergence Analysis and Parallel Implementation
by: Xu, Jintao, et al.
Published: (2023)
by: Xu, Jintao, et al.
Published: (2023)
ADMM for Structured Fractional Minimization
by: Yuan, Ganzhao
Published: (2024)
by: Yuan, Ganzhao
Published: (2024)
FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning
by: Song, Yongcun, et al.
Published: (2024)
by: Song, Yongcun, et al.
Published: (2024)
Rethinking PCA Through Duality
by: Quan, Jan, et al.
Published: (2025)
by: Quan, Jan, et al.
Published: (2025)
Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
by: Hu, Jie, et al.
Published: (2024)
by: Hu, Jie, et al.
Published: (2024)
Joint Cooperative and Non-Cooperative Localization in WSNs with Distributed Scaled Proximal ADMM Algorithms
by: Zhu, Qiaojia, et al.
Published: (2025)
by: Zhu, Qiaojia, et al.
Published: (2025)
A Riemannian ADMM
by: Li, Jiaxiang, et al.
Published: (2022)
by: Li, Jiaxiang, et al.
Published: (2022)
The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach
by: Song, Yongcun, et al.
Published: (2023)
by: Song, Yongcun, et al.
Published: (2023)
$O(1/k)$ Finite-Time Bound for Non-Linear Two-Time-Scale Stochastic Approximation
by: Chandak, Siddharth
Published: (2025)
by: Chandak, Siddharth
Published: (2025)
Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors
by: Weng, Yidou, et al.
Published: (2024)
by: Weng, Yidou, et al.
Published: (2024)
Duality and Policy Evaluation in Distributionally Robust Bayesian Diffusion Control
by: Blanchet, Jose, et al.
Published: (2025)
by: Blanchet, Jose, et al.
Published: (2025)
A Unified Kantorovich Duality for Multimarginal Optimal Transport
by: Cheryala, Yehya, et al.
Published: (2026)
by: Cheryala, Yehya, et al.
Published: (2026)
Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
by: Hu, Jie, et al.
Published: (2024)
by: Hu, Jie, et al.
Published: (2024)
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events
by: Taboga, Vincent, et al.
Published: (2023)
by: Taboga, Vincent, et al.
Published: (2023)
Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks
by: Nofshin, Eura, et al.
Published: (2024)
by: Nofshin, Eura, et al.
Published: (2024)
Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning
by: Li, Zihao, et al.
Published: (2024)
by: Li, Zihao, et al.
Published: (2024)
Learning Weakly Communicating Average-Reward CMDPs: Strong Duality and Improved Regret
by: Yu, Kihyun, et al.
Published: (2026)
by: Yu, Kihyun, et al.
Published: (2026)
Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM
by: Noah, Yoav, et al.
Published: (2023)
by: Noah, Yoav, et al.
Published: (2023)
PACSBO: Probably approximately correct safe Bayesian optimization
by: Tokmak, Abdullah, et al.
Published: (2024)
by: Tokmak, Abdullah, et al.
Published: (2024)
Practical Bayesian Algorithm Execution via Posterior Sampling
by: Cheng, Chu Xin, et al.
Published: (2024)
by: Cheng, Chu Xin, et al.
Published: (2024)
Safe Bayesian optimization across noise models via scenario programming
by: Tokmak, Abdullah, et al.
Published: (2025)
by: Tokmak, Abdullah, et al.
Published: (2025)
Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise
by: Chandak, Siddharth, et al.
Published: (2025)
by: Chandak, Siddharth, et al.
Published: (2025)
Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis
by: Chandak, Siddharth, et al.
Published: (2026)
by: Chandak, Siddharth, et al.
Published: (2026)
Refined Analysis of Federated Averaging and Federated Richardson-Romberg
by: Mangold, Paul, et al.
Published: (2024)
by: Mangold, Paul, et al.
Published: (2024)
Maximizing Reliability with Bayesian Optimization
by: Buckingham, Jack M., et al.
Published: (2026)
by: Buckingham, Jack M., et al.
Published: (2026)
Bayesian Optimization of Bilevel Problems
by: Ekmekcioglu, Omer, et al.
Published: (2024)
by: Ekmekcioglu, Omer, et al.
Published: (2024)
BoFire: Bayesian Optimization Framework Intended for Real Experiments
by: Dürholt, Johannes P., et al.
Published: (2024)
by: Dürholt, Johannes P., et al.
Published: (2024)
Function Gradient Approximation with Random Shallow ReLU Networks with Control Applications
by: Lamperski, Andrew, et al.
Published: (2024)
by: Lamperski, Andrew, et al.
Published: (2024)
Bayesian Optimisation: Which Constraints Matter?
by: Lin, Xietao Wang, et al.
Published: (2025)
by: Lin, Xietao Wang, et al.
Published: (2025)
Preferential Multi-Objective Bayesian Optimization
by: Astudillo, Raul, et al.
Published: (2024)
by: Astudillo, Raul, et al.
Published: (2024)
Non-Myopic Multifidelity Bayesian Optimization
by: Di Fiore, Francesco, et al.
Published: (2022)
by: Di Fiore, Francesco, et al.
Published: (2022)
Similar Items
-
Connecting Federated ADMM to Bayes
by: Swaroop, Siddharth, et al.
Published: (2025) -
Federated Learning Using Three-Operator ADMM
by: Kant, Shashi, et al.
Published: (2022) -
Variational Learning is Effective for Large Deep Networks
by: Shen, Yuesong, et al.
Published: (2024) -
Distributed Event-Based Learning via ADMM
by: Er, Guner Dilsad, et al.
Published: (2024) -
Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
by: Lin, Junan, et al.
Published: (2026)