high-dimensional analysis

The Breakdown of Gaussian Universality in Classification of High-dimensional Linear Factor Mixtures ICLR 2025 To Clip or not to Clip: the Dynamics of SGD with Gradient Clipping in High-Dimensions ICLR 2025 Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data NeurIPS 2024 Variable Importance in High-Dimensional Settings Requires Grouping AAAI 2024 Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization NeurIPS 2023 Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation ICML 2023 Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron ICML 2023 Margin-based sampling in high dimensions: When being active is less efficient than staying passive ICML 2023 Optimal Sparse Recovery with Decision Stumps AAAI 2023 Anisotropic Random Feature Regression in High Dimensions ICLR 2022 A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions AISTATS 2022 Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm ICML 2022 High-dimensional limit theorems for SGD: Effective dynamics and critical scaling NeurIPS 2022 High-Dimensional Projection Pursuit: Outer Bounds and Applications to Interpolation in Neural Networks COLT 2022 Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions NeurIPS 2022 KoPA: Automated Kronecker Product Approximation JMLR 2022 Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks NeurIPS 2022 How rotational invariance of common kernels prevents generalization in high dimensions ICML 2021 Kernel regression in high dimensions: Refined analysis beyond double descent AISTATS 2021 Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions NeurIPS 2021 On the proliferation of support vectors in high dimensions AISTATS 2021 Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification NeurIPS 2020 Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization NeurIPS 2020 Phase retrieval in high dimensions: Statistical and computational phase transitions NeurIPS 2020 Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View NeurIPS 2020