Imagen de Portada

Towards an Approximation Theory of Observable Operator Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Anyszka, Wojciech
Formato:	Preprint
Publicado:	2024
Materias:	Probability Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Tighter Performance Theory of FedExProx
por: Anyszka, Wojciech, et al.
Publicado: (2024)

Towards Continuous-Time Approximations for Stochastic Gradient Descent without Replacement
por: Perko, Stefan
Publicado: (2025)

Steady-State Behavior of Constant-Stepsize Stochastic Approximation: Gaussian Approximation and Tail Bounds
por: Wang, Zedong, et al.
Publicado: (2026)

Gaussian Approximation for Asynchronous Q-learning
por: Rubtsov, Artemy, et al.
Publicado: (2026)

Concept activation vectors: a unifying view and adversarial attacks
por: Schnoor, Ekkehard, et al.
Publicado: (2025)

Stochastic Operator Network: A Stochastic Maximum Principle Based Approach to Operator Learning
por: Bausback, Ryan, et al.
Publicado: (2025)

Approximating G(t)/GI/1 queues with deep learning
por: Sherzer, Eliran, et al.
Publicado: (2024)

Performance of Rank-One Tensor Approximation on Incomplete Data
por: Lebeau, Hugo
Publicado: (2025)

Approximation to Deep Q-Network by Stochastic Delay Differential Equations
por: Lu, Jianya, et al.
Publicado: (2025)

A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation
por: Lebeau, Hugo, et al.
Publicado: (2024)

Stochastic Port-Hamiltonian Neural Networks: Universal Approximation with Passivity Guarantees
por: Di Persio, Luca, et al.
Publicado: (2026)

A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging
por: Khodadadian, Sajad, et al.
Publicado: (2025)

Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
por: Labarthe, Aldric
Publicado: (2026)

Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features
por: Kotsiopoulos, Konstandinos, et al.
Publicado: (2023)

A Review of the Receiver Operating Characteristic Curve and a Proof About the Area Beneath It
por: Redolfi, Steven
Publicado: (2026)

A Learning-Based Superposition Operator for Non-Renewal Arrival Processes in Queueing Networks
por: Sherzer, Eliran
Publicado: (2026)

How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability
por: Zhou, Yijin, et al.
Publicado: (2024)

Approximation and interpolation of deep neural networks
por: Constantinescu, Vlad-Raul, et al.
Publicado: (2023)

Neural Expectation Operators
por: Qi, Qian
Publicado: (2025)

$α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
por: Schnoor, Ekkehard, et al.
Publicado: (2026)

Polynomial Scaling is Possible For Neural Operator Approximations of Structured Families of BSDEs
por: Furuya, Takashi, et al.
Publicado: (2024)

Response Theory via Generative Score Modeling
por: Giorgini, Ludovico Theo, et al.
Publicado: (2024)

Markov Chain Variance Estimation: A Stochastic Approximation Approach
por: Agrawal, Shubhada, et al.
Publicado: (2024)

Multivariate Gaussian Approximation for Random Forest via Region-based Stabilization
por: Shi, Zhaoyang, et al.
Publicado: (2024)

Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise
por: Agrawal, Shubhada, et al.
Publicado: (2026)

Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting
por: Ustimenko, Aleksei, et al.
Publicado: (2023)

Improved Approximation Algorithms for Orthogonally Constrained Problems Using Semidefinite Optimization
por: Cory-Wright, Ryan, et al.
Publicado: (2025)

Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
por: Han, Yuze, et al.
Publicado: (2024)

Chernoff Bounds for Tensor Expanders on Riemannian Manifolds Using Graph Laplacian Approximation
por: Chang, Shih-Yu
Publicado: (2024)

Finite-Dimensional Gaussian Approximation for Deep Neural Networks: Universality in Random Weights
por: Balasubramanian, Krishnakumar, et al.
Publicado: (2025)

Approximating Langevin Monte Carlo with ResNet-like Neural Network architectures
por: Miranda, Charles, et al.
Publicado: (2023)

Note on Martingale Theory and Applications
por: Zou, Xiandong
Publicado: (2026)

Designing Observables for Measurements with Deep Learning
por: Long, Owen, et al.
Publicado: (2023)

Probabilistic Conformal Prediction with Approximate Conditional Validity
por: Plassier, Vincent, et al.
Publicado: (2024)

Approximating the Total Variation Distance between Gaussians
por: Bhattacharyya, Arnab, et al.
Publicado: (2025)

Decision-Focused Bias Correction for Fluid Approximation
por: Er, Can, et al.
Publicado: (2025)

Learning with Expected Signatures: Theory and Applications
por: Lucchese, Lorenzo, et al.
Publicado: (2025)

A Unified Theory of $θ$-Expectations
por: Qi, Qian
Publicado: (2025)

Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence
por: de Frutos, José Manuel, et al.
Publicado: (2025)

Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing
por: Zhang, Yihan, et al.
Publicado: (2023)