:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	van Dijk, Marten, Nguyen, Nhuong V., Nguyen, Toan N., Nguyen, Lam M., Nguyen, Phuong Ha
Formato:	Preprint
Publicado:	2021
Materias:	Machine Learning Optimization and Control
Acceso en línea:	https://arxiv.org/abs/2102.09030
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting
por: Nguyen, Duc Toan, et al.
Publicado: (2025)

On the Convergence of DP-SGD with Adaptive Clipping
por: Shulgin, Egor, et al.
Publicado: (2024)

On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error
por: Nguyen, Quang Minh, et al.
Publicado: (2022)

Fréchet Regression on the Bures-Wasserstein Manifold
por: Nguyen, Duc Toan, et al.
Publicado: (2026)

Learning to Shuffle: Block Reshuffling and Reversal Schemes for Stochastic Optimization
por: Nguyen, Lam M., et al.
Publicado: (2026)

Shuffling Momentum Gradient Algorithm for Convex Optimization
por: Tran, Trang H., et al.
Publicado: (2024)

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD
por: Ertan, Murat Bilgehan, et al.
Publicado: (2026)

Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization
por: Tran-Dinh, Quoc, et al.
Publicado: (2024)

Distributional Surgery for Language Model Activations
por: Nguyen, Bao, et al.
Publicado: (2025)

Provably data-driven projection method for quadratic programming
por: Nguyen, Anh Tuan, et al.
Publicado: (2025)

How to Set $β_1, β_2$ in Adam: An Online Learning Perspective
por: Nguyen, Quan
Publicado: (2025)

Lean and Mean Adaptive Optimization via Subset-Norm and Subspace-Momentum with Convergence Guarantees
por: Nguyen, Thien Hang, et al.
Publicado: (2024)

FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees
por: Nguyen, Hoang T., et al.
Publicado: (2025)

A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives
por: Nguyen, Ti Ti, et al.
Publicado: (2025)

Gap Safe Screening Rules for Fast Training of Robust Support Vector Machines under Feature Noise
por: Nguyen, Tan-Hau, et al.
Publicado: (2026)

Efficient Online Large-Margin Classification via Dual Certificates
por: Ho-Nguyen, Nam, et al.
Publicado: (2025)

Coverage-Validity-Aware Algorithmic Recourse
por: Bui, Ngoc, et al.
Publicado: (2023)

HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
por: Tran, Trinh, et al.
Publicado: (2026)

Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds
por: van Dijk, Marten, et al.
Publicado: (2026)

Stochastic Constrained Decentralized Optimization for Machine Learning with Fewer Data Oracles: a Gradient Sliding Approach
por: Nguyen, Hoang Huy, et al.
Publicado: (2024)

Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels
por: Nguyen, Khai, et al.
Publicado: (2026)

Optimizing Stochastic Gradient Push under Broadcast Communications
por: Nguyen, Tuan, et al.
Publicado: (2026)

Controllable Expensive Multi-objective Learning with Warm-starting Bayesian Optimization
por: Nguyen, Quang-Huy, et al.
Publicado: (2023)

Chebyshev Center-Based Direction Selection for Multi-Objective Optimization and Training PINNs
por: Yoon, Hoyeol, et al.
Publicado: (2026)

Intrinsic Decentralized Stochastic Riemannian Optimization on Manifolds with Bounded Sectional Curvature
por: Nguyen, Duc Toan, et al.
Publicado: (2026)

Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networks
por: Nguyen, Duong Thuy Anh, et al.
Publicado: (2024)

Decoupled Continuous-Time Reinforcement Learning via Hamiltonian Flow
por: Nguyen, Minh
Publicado: (2026)

VFOG: Variance-Reduced Fast Optimistic Gradient Methods for a Class of Nonmonotone Generalized Equations
por: Tran-Dinh, Quoc, et al.
Publicado: (2025)

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
por: Kuhn, Daniel, et al.
Publicado: (2019)

Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity
por: Nguyen, Quan, et al.
Publicado: (2024)

Tight Robustness Certificates and Wasserstein Distributional Attacks for Deep Neural Networks
por: Le, Bach C., et al.
Publicado: (2025)

SCOPE: Spectral Concentration by Distributionally Robust Joint Covariance-Precision Estimation
por: Chen, Renjie, et al.
Publicado: (2025)

SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization
por: Dahan, Tehila, et al.
Publicado: (2023)

A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints
por: Tuan, Tran Anh, et al.
Publicado: (2024)

Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection
por: Nguyen, Manh Hung, et al.
Publicado: (2024)

Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty
por: Lin, Zikun, et al.
Publicado: (2026)

A Differential and Pointwise Control Approach to Reinforcement Learning
por: Nguyen, Minh, et al.
Publicado: (2024)

Sign-SGD via Parameter-Free Optimization
por: Medyakov, Daniil, et al.
Publicado: (2025)

From PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees
por: Xie, Shengping, et al.
Publicado: (2025)

A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set
por: Yue, Man-Chung, et al.
Publicado: (2024)