Image de couverture de livre

Efficient Trajectory Inference in Wasserstein Space Using Consecutive Averaging

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Banerjee, Amartya, Lee, Harlin, Sharon, Nir, Moosmüller, Caroline
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Numerical Analysis Optimization and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods
par: O'Leary-Roseberry, Thomas, et autres
Publié: (2024)

OptEMA: Adaptive Exponential Moving Average for Stochastic Optimization with Zero-Noise Optimality
par: Yuan, Ganzhao
Publié: (2026)

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems
par: Dereich, Steffen, et autres
Publié: (2025)

Using Linearized Optimal Transport to Predict the Evolution of Stochastic Particle Systems
par: Karris, Nicholas, et autres
Publié: (2024)

Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models
par: Cohen, Jeremy E., et autres
Publié: (2024)

Minimisation of Submodular Functions Using Gaussian Zeroth-Order Random Oracles
par: Farzin, Amir Ali, et autres
Publié: (2025)

Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
par: Dereich, Steffen, et autres
Publié: (2024)

On the numerical reliability of nonsmooth autodiff: a MaxPool case study
par: Boustany, Ryan
Publié: (2024)

A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
par: Mishra, Neel, et autres
Publié: (2024)

Subhomogeneous Deep Equilibrium Models
par: Sittoni, Pietro, et autres
Publié: (2024)

Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance
par: Stollenwerk, Alexander, et autres
Publié: (2024)

Anderson Acceleration in Nonsmooth Problems: Local Convergence via Active Manifold Identification
par: Li, Kexin, et autres
Publié: (2024)

KANtrol: A Physics-Informed Kolmogorov-Arnold Network Framework for Solving Multi-Dimensional and Fractional Optimal Control Problems
par: Aghaei, Alireza Afzal
Publié: (2024)

Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models
par: Tomasetto, Matteo, et autres
Publié: (2024)

Cubic regularized subspace Newton for non-convex optimization
par: Zhao, Jim, et autres
Publié: (2024)

Towards Quantifying the Preconditioning Effect of Adam
par: Das, Rudrajit, et autres
Publié: (2024)

Super Gradient Descent: Global Optimization requires Global Gradient
par: Achour, Seifeddine
Publié: (2024)

Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models
par: Tomasetto, Matteo, et autres
Publié: (2024)

Quantitative Convergences of Lie Group Momentum Optimizers
par: Kong, Lingkai, et autres
Publié: (2024)

Learning incomplete factorization preconditioners for GMRES
par: Häusner, Paul, et autres
Publié: (2024)

A note on continuous-time online learning
par: Ying, Lexing
Publié: (2024)

Symmetry & Critical Points
par: Arjevani, Yossi
Publié: (2024)

ADMM for Structured Fractional Minimization
par: Yuan, Ganzhao
Publié: (2024)

Lyapunov Analysis For Monotonically Forward-Backward Accelerated Algorithms
par: Fu, Mingwei, et autres
Publié: (2024)

Suboptimality bounds for trace-bounded SDPs enable a faster and scalable low-rank SDP solver SDPLR+
par: Huang, Yufan, et autres
Publié: (2024)

Dimensionality Reduction Techniques for Global Bayesian Optimisation
par: Long, Luo, et autres
Publié: (2024)

Mathematical Opportunities in Digital Twins (MATH-DT)
par: Antil, Harbir
Publié: (2024)

Modeling AdaGrad, RMSProp, and Adam with Integro-Differential Equations
par: Heredia, Carlos
Publié: (2024)

Learning truly monotone operators with applications to nonlinear inverse problems
par: Belkouchi, Younes, et autres
Publié: (2024)

A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations
par: Liu, Shu, et autres
Publié: (2024)

Multi-level Optimal Control with Neural Surrogate Models
par: Kalise, Dante, et autres
Publié: (2024)

End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
par: Ma, Shaocong, et autres
Publié: (2024)

Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models
par: Zhang, Fangzhao, et autres
Publié: (2024)

Nonlinear Assimilation via Score-based Sequential Langevin Sampling
par: Ding, Zhao, et autres
Publié: (2024)

Dynamic Proximal Gradient Algorithms for Schatten-$p$ Quasi-Norm Regularized Problems
par: Shen, Weiping, et autres
Publié: (2026)

Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
par: Niu, Chengmei, et autres
Publié: (2025)

Scalable Acceleration for Classification-Based Derivative-Free Optimization
par: Han, Tianyi, et autres
Publié: (2023)

Why is Normalization Preferred? A Worst-Case Complexity Theory for Stochastically Preconditioned SGD under Heavy-Tailed Noise
par: Fang, Yuchen, et autres
Publié: (2026)

Natural Riemannian gradient for learning functional tensor networks
par: Klug, Nikolas, et autres
Publié: (2026)

Error Feedback Can Accurately Compress Preconditioners
par: Modoranu, Ionut-Vlad, et autres
Publié: (2023)