Copertina

How to beat a Bayesian adversary

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ding, Zihan, Jin, Kexin, Latz, Jonas, Liu, Chenguang
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Optimization and Control Computation 90C15, 65C35, 68T07
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Losing momentum in continuous-time stochastic optimisation
di: Jin, Kexin, et al.
Pubblicazione: (2022)

BROS: Bias-Corrected Randomized Subspaces for Memory-Efficient Single-Loop Bilevel Optimization
di: Zhang, Hengrui, et al.
Pubblicazione: (2026)

Faster Adaptive Optimization via Expected Gradient Outer Product Reparameterization
di: DePavia, Adela, et al.
Pubblicazione: (2025)

A Layer Separation Optimization Framework for Cross-Entropy Training in Deep Learning
di: Liu, Yaru, et al.
Pubblicazione: (2026)

Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems
di: Lapucci, Matteo, et al.
Pubblicazione: (2024)

Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models
di: Lapucci, Matteo, et al.
Pubblicazione: (2024)

Error Bound Analysis for the Regularized Loss of Deep Linear Neural Networks
di: Chen, Po, et al.
Pubblicazione: (2025)

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies
di: Roith, Tim, et al.
Pubblicazione: (2025)

Stochastic Finite Volume Method for Uncertainty Management in Gas Pipeline Network Flows
di: Kazi, Saif R., et al.
Pubblicazione: (2024)

Representation and Regression Problems in Neural Networks: Relaxation, Generalization, and Numerics
di: Liu, Kang, et al.
Pubblicazione: (2024)

Power Homotopy for Zeroth-Order Non-Convex Optimizations
di: Xu, Chen
Pubblicazione: (2025)

Global Optimization with A Power-Transformed Objective and Gaussian Smoothing
di: Xu, Chen
Pubblicazione: (2024)

A Two-Phase Adaptive Balanced Penalty Method for Controllable Pareto Front Learning under Split Feasibility Conditions
di: Hoang, Nguyen Viet, et al.
Pubblicazione: (2026)

Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications
di: Lee, Youngkyu, et al.
Pubblicazione: (2024)

Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies
di: Kopaničáková, Alena, et al.
Pubblicazione: (2023)

Objective Value Change and Shape-Based Accelerated Optimization for the Neural Network Approximation
di: Xie, Pengcheng, et al.
Pubblicazione: (2025)

An Augmented Lagrangian Method for Training Recurrent Neural Networks
di: Wang, Yue, et al.
Pubblicazione: (2024)

SUDA-Muon: Structural Design Principles and Boundaries for Fully Decentralized Muon
di: Zhang, Hengrui, et al.
Pubblicazione: (2026)

Supplementary Materials to Graph Convolutional Branch and Bound
di: Sciandra, Lorenzo, et al.
Pubblicazione: (2024)

Sample-wise Constrained Learning via a Sequential Penalty Approach with Applications in Image Processing
di: Lanzillotta, Francesca, et al.
Pubblicazione: (2026)

Convergence, design and training of continuous-time dropout as a random batch method
di: Álvarez-López, Antonio, et al.
Pubblicazione: (2025)

Preconditioned subgradient method for composite optimization: overparameterization and fast convergence
di: Díaz, Mateo, et al.
Pubblicazione: (2025)

SVD-Preconditioned Gradient Descent Method for Solving Nonlinear Least Squares Problems
di: Chang, Zhipeng, et al.
Pubblicazione: (2026)

Convergence of gradient descent for deep neural networks
di: Chatterjee, Sourav
Pubblicazione: (2022)

Subsampling Error in Stochastic Gradient Langevin Diffusions
di: Jin, Kexin, et al.
Pubblicazione: (2023)

Edge-Wise Graph-Instructed Neural Networks
di: Della Santa, Francesco, et al.
Pubblicazione: (2024)

Federated Learning with Convex Global and Local Constraints
di: He, Chuan, et al.
Pubblicazione: (2023)

A Unified Analysis on the Subgradient Upper Bounds for the Subgradient Methods Minimizing Composite Nonconvex, Nonsmooth and Non-Lipschitz Functions
di: Zhu, Daoli, et al.
Pubblicazione: (2023)

Exponential convergence rates for momentum stochastic gradient descent in the overparametrized setting
di: Gess, Benjamin, et al.
Pubblicazione: (2023)

The Hard-Constraint PINNs for Interface Optimal Control Problems
di: Lai, Ming-Chih, et al.
Pubblicazione: (2023)

On the boundedness of the sequence generated by minibatch stochastic gradient descent
di: Bauschke, Heinz H., et al.
Pubblicazione: (2025)

Progressive Power Homotopy for Non-convex Optimization
di: Xu, Chen
Pubblicazione: (2026)

Quantum-Inspired DRL Approach with LSTM and OU Noise for Cut Order Planning Optimization
di: Chrisnanto, Yulison Herry, et al.
Pubblicazione: (2025)

Tracking the Median of Gradients with a Stochastic Proximal Point Method
di: Schaipp, Fabian, et al.
Pubblicazione: (2024)

DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime
di: Park, Jongho, et al.
Pubblicazione: (2023)

Riemannian Federated Learning via Averaging Gradient Streams
di: Huang, Zhenwei, et al.
Pubblicazione: (2024)

Distributed Gradient-Regularized Newton Method: Scheduled Consensus and O(epsilon^{-1}) Global Iteration Complexity
di: Hu, Wei, et al.
Pubblicazione: (2026)

An Algebraically Converging Stochastic Gradient Descent Algorithm for Global Optimization
di: Engquist, Björn, et al.
Pubblicazione: (2022)

System stabilization with policy optimization on unstable latent manifolds
di: Werner, Steffen W. R., et al.
Pubblicazione: (2024)

Useful Compact Representations for Data-Fitting
di: Brust, Johannes J.
Pubblicazione: (2024)