:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chen, Cheng, Yang, Junjie, Zhou, Yi
Format:	Preprint
Published:	2020
Subjects:	Machine Learning Optimization and Control
Online Access:	https://arxiv.org/abs/2011.06702
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Regularized Gauss-Newton for Optimizing Overparameterized Neural Networks
by: Adeoye, Adeyemi D., et al.
Published: (2024)

Nested Stochastic Algorithm for Generalized Sinkhorn distance-Regularized Distributionally Robust Optimization
by: Yang, Yufeng, et al.
Published: (2025)

Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
by: Tucat, Matteo, et al.
Published: (2024)

Regularized Adaptive Momentum Dual Averaging with an Efficient Inexact Subproblem Solver for Training Structured Neural Network
by: Huang, Zih-Syuan, et al.
Published: (2024)

Optimization Over Trained Neural Networks: Taking a Relaxing Walk
by: Tong, Jiatai, et al.
Published: (2024)

Towards Guided Descent: Optimization Algorithms for Training Neural Networks At Scale
by: Nagwekar, Ansh
Published: (2025)

Curse of Dimensionality in Neural Network Optimization
by: Na, Sanghoon, et al.
Published: (2025)

Homotopy Relaxation Training Algorithms for Infinite-Width Two-Layer ReLU Neural Networks
by: Yang, Yahong, et al.
Published: (2023)

Model-Based Diffusion for Trajectory Optimization
by: Pan, Chaoyi, et al.
Published: (2024)

Mean-Field Limits for Two-Layer Neural Networks Trained with Consensus-Based Optimization
by: De Deyn, William, et al.
Published: (2025)

Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate
by: Pham, Hung, et al.
Published: (2025)

How Neural Networks Learn the Support is an Implicit Regularization Effect of SGD
by: Beneventano, Pierfrancesco, et al.
Published: (2024)

Distributionally Robust Multi-Objective Optimization
by: Yang, Yufeng, et al.
Published: (2026)

Training Neural ODEs Using Fully Discretized Simultaneous Optimization
by: Shapovalova, Mariia, et al.
Published: (2025)

Relaxation-Informed Training of Neural Network Surrogate Models
by: Tsay, Calvin
Published: (2026)

Adaptive Momentum and Nonlinear Damping for Neural Network Training
by: Karoni, Aikaterini, et al.
Published: (2026)

Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse
by: Jacot, Arthur, et al.
Published: (2024)

Verifying Properties of Binary Neural Networks Using Sparse Polynomial Optimization
by: Yang, Jianting, et al.
Published: (2024)

On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
by: Liang, Ling, et al.
Published: (2024)

Compression-aware Training of Neural Networks using Frank-Wolfe
by: Zimmer, Max, et al.
Published: (2022)

A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees
by: Zhou, Yuhao, et al.
Published: (2025)

Deceptive Sequential Decision-Making via Regularized Policy Optimization
by: Kim, Yerin, et al.
Published: (2025)

(Almost) Smooth Sailing: Towards Numerical Stability of Neural Networks Through Differentiable Regularization of the Condition Number
by: Nenov, Rossen, et al.
Published: (2024)

Convex Formulations for Training Two-Layer ReLU Neural Networks
by: Prakhya, Karthik, et al.
Published: (2024)

Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks
by: Sanguin, Gabriele, et al.
Published: (2025)

SGD with Partial Hessian for Deep Neural Networks Optimization
by: Sun, Ying, et al.
Published: (2024)

Federated Optimization with Doubly Regularized Drift Correction
by: Jiang, Xiaowen, et al.
Published: (2024)

FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling
by: Chen, Cheng, et al.
Published: (2020)

Deterministic Trajectory Optimization through Probabilistic Optimal Control
by: Filabadi, Mohammad Mahmoudi, et al.
Published: (2024)

A Non-Monotone Preconditioned Trust-Region Method for Neural Network Training
by: Angino, Andrea, et al.
Published: (2026)

Dual Natural Gradient Descent for Scalable Training of Physics-Informed Neural Networks
by: Jnini, Anas, et al.
Published: (2025)

Neighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks
by: Noel, Molly, et al.
Published: (2025)

Optimality-Informed Neural Networks for Solving Parametric Optimization Problems
by: Hoffmann, Matthias K., et al.
Published: (2025)

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

Neural Approximators for Low-Thrust Trajectory Transfer Cost and Reachability
by: Zhang, Zhong, et al.
Published: (2025)

On Generalization and Regularization via Wasserstein Distributionally Robust Optimization
by: Wu, Qinyu, et al.
Published: (2022)

Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization
by: Yang, Yiran, et al.
Published: (2025)

Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural Networks
by: Bernardelli, Ambrogio Maria, et al.
Published: (2022)

Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation
by: Bylinkin, Dmitry, et al.
Published: (2025)

Convergence of Implicit Gradient Descent for Training Two-Layer Physics-Informed Neural Networks
by: Xu, Xianliang, et al.
Published: (2024)