conftrace_

curse of dimensionality

61 papers

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Also known as

COD

Co-occurring keywords

partial differential equation (222) deep neural network (1803) approximation theory (77) convergence rate (607) randomized smoothing (87) neural network (6616) nonparametric regression (111) optimal transport (948) dimensionality reduction (720) graphical model (864)

Papers

Finite Expression Method for Solving High-Dimensional Partial Differential Equations JMLR 2025
Robust Point Matching with Distance Profiles JMLR 2025
Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary JMLR 2025
Physics-Informed Deep Learning and Compressive Collocation for High-Dimensional Diffusion-Reaction Equations: Practical Existence Theory and Numerics JMLR 2025
Adaptivity of Diffusion Models to Manifold Structures AISTATS 2024
On Sufficient Graphical Models JMLR 2024
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds JMLR 2024
Random Smoothing Regularization in Kernel Gradient Descent Learning JMLR 2024
On Differentially Private Subspace Estimation in a Distribution-Free Setting NIPS 2024
Cylindrical Thompson Sampling for High-Dimensional Bayesian Optimization AISTATS 2024
Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality JMLR 2024
Classification with Deep Neural Networks and Logistic Loss JMLR 2024
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks NIPS 2024
Breaking the curse of dimensionality in structured density estimation NIPS 2024
On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory NIPS 2024
Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering JMLR 2023
Operator learning with PCA-Net: upper and lower complexity bounds JMLR 2023
Over-parameterized Deep Nonparametric Regression for Dependent Data with Its Applications to Reinforcement Learning JMLR 2023
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models JMLR 2023
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective ICML 2023
Minimax estimation of discontinuous optimal transport maps: The semi-discrete case ICML 2023
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories ICML 2023
Learning Optimal Feedback Operators and their Sparse Polynomial Approximations JMLR 2023
Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality JMLR 2023
Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input ICML 2023