high-dimensional learning

Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics ICLR 2025 Learnability of high-dimensional targets by two-parameter models and gradient flow NeurIPS 2024 Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning NeurIPS 2024 Pruning is Optimal for Learning Sparse Features in High-Dimensions COLT 2024 Revisiting Differentially Private ReLU Regression NeurIPS 2024 Robust Methods for High-Dimensional Linear Learning JMLR 2023 Estimation and Inference with Trees and Forests in High Dimensions COLT 2020 High-Dimensional Convolutional Networks for Geometric Pattern Recognition CVPR 2020 Robust high dimensional learning for Lipschitz and convex losses JMLR 2020 Differentially Private Algorithms for Learning Mixtures of Separated Gaussians NeurIPS 2019 Dimension-Free Error Bounds from Random Projections AAAI 2019 Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables AISTATS 2018 Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers ICML 2014 Feature Clustering for Accelerating Parallel Coordinate Descent NeurIPS 2012 Greedy Algorithms for Structurally Constrained High Dimensional Problems NeurIPS 2011 Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity AISTATS 2010 Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach AISTATS 2010 Nonparametric regression and classification with joint sparsity constraints NeurIPS 2008 A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians JMLR 2007