neural network approximation

Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection JMLR 2026 PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations ICLR 2025 Rethinking Neural-based Matrix Inversion: Why can’t, and Where can AISTATS 2025 DataSP: A Differential All-to-All Shortest Path Algorithm for Learning Costs and Predicting Paths with Context UAI 2024 Learning to Solve Bilevel Programs with Binary Tender ICLR 2024 Neur2BiLO: Neural Bilevel Optimization NeurIPS 2024 Neural Network Approximation for Pessimistic Offline Reinforcement Learning AAAI 2024 Policy Evaluation for Reinforcement Learning from Human Feedback: A Sample Complexity Analysis AISTATS 2024 Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian distributions ICLR 2024 The Galerkin method beats Graph-Based Approaches for Spectral Algorithms AISTATS 2024 Computational Doob h-transforms for Online Filtering of Discretely Observed Diffusions ICML 2023 Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network ICML 2023 Limitations on approximation by deep and shallow neural networks JMLR 2023 Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective ICML 2023 On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network ICML 2023 Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces JMLR 2023 Parametrizing Product Shape Manifolds by Composite Networks ICLR 2023 Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime ICML 2022 Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons JMLR 2022 Finite-Time Analysis of Adaptive Temporal Difference Learning with Deep Neural Networks NeurIPS 2022 Quantile regression with ReLU Networks: Estimators and minimax rates JMLR 2022 Size and depth of monotone neural networks: interpolation and approximation NeurIPS 2022 Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers NeurIPS 2022 Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups NeurIPS 2022 A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Partial Differential Equations COLT 2021