conftrace_

high-dimensional analysis

33 papers

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Co-occurring keywords

neural network (6616) stochastic gradient descent (1091) generalization error (285) feature selection (649) convex optimization (1321) high-dimensional regression (142) stochastic differential equation (181) high-dimensional statistics (145) learning theory (519) kernel ridge regression (111)

Papers

Variable Importance in High-Dimensional Settings Requires Grouping AAAI 2024
Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data NIPS 2024
Margin-based sampling in high dimensions: When being active is less efficient than staying passive ICML 2023
Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation ICML 2023
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization NIPS 2023
Optimal Sparse Recovery with Decision Stumps AAAI 2023
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron ICML 2023
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions NIPS 2022
Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm ICML 2022
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks NIPS 2022
High-Dimensional Projection Pursuit: Outer Bounds and Applications to Interpolation in Neural Networks COLT 2022
A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions AISTATS 2022
High-dimensional limit theorems for SGD: Effective dynamics and critical scaling NIPS 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
On the proliferation of support vectors in high dimensions AISTATS 2021
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions NIPS 2021
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification NIPS 2020
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View NIPS 2020
Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization NIPS 2020
A Solvable High-Dimensional Model of GAN NIPS 2019
Lifting high-dimensional non-linear models with Gaussian regressors AISTATS 2019
Direct Estimation of Differential Functional Graphical Models NIPS 2019
Learning ReLUs via Gradient Descent NIPS 2017
Variable Importance Using Decision Trees NIPS 2017