Saved in:
| Main Authors: | Bond, Andrew, Dogan, Zafer |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.00498 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Implicitly Normalized Online PCA: A Regularized Algorithm with Exact High-Dimensional Dynamics
by: Demir, Samet, et al.
Published: (2025)
by: Demir, Samet, et al.
Published: (2025)
Learnability and Competition in High-Dimensional Multi-Component ICA
by: Genc, Eser Ilke, et al.
Published: (2026)
by: Genc, Eser Ilke, et al.
Published: (2026)
Learning Rate Should Scale Inversely with High-Order Data Moments in High-Dimensional Online Independent Component Analysis
by: Gultekin, M. Oguzhan, et al.
Published: (2025)
by: Gultekin, M. Oguzhan, et al.
Published: (2025)
Optimal Attention Temperature Improves the Robustness of In-Context Learning under Distribution Shift in High Dimensions
by: Demir, Samet, et al.
Published: (2025)
by: Demir, Samet, et al.
Published: (2025)
Asymptotic Analysis of Two-Layer Neural Networks after One Gradient Step under Gaussian Mixtures Data with Structure
by: Demir, Samet, et al.
Published: (2025)
by: Demir, Samet, et al.
Published: (2025)
Learning Beyond the Gaussian Data: Learning Dynamics of Neural Networks on an Expressive and Cumulant-Controllable Data Model
by: Ure, Onat, et al.
Published: (2026)
by: Ure, Onat, et al.
Published: (2026)
Input-Label Correlation Governs a Linear-to-Nonlinear Transition in Random Features under Spiked Covariance
by: Demir, Samet, et al.
Published: (2024)
by: Demir, Samet, et al.
Published: (2024)
Asymptotic Study of In-context Learning with Random Transformers through Equivalent Models
by: Demir, Samet, et al.
Published: (2025)
by: Demir, Samet, et al.
Published: (2025)
How Data Mixing Shapes In-Context Learning: Asymptotic Equivalence for Transformers with MLPs
by: Demir, Samet, et al.
Published: (2025)
by: Demir, Samet, et al.
Published: (2025)
SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer
by: Takida, Yuhta, et al.
Published: (2023)
by: Takida, Yuhta, et al.
Published: (2023)
Leveraging Axis-Aligned Subspaces for High-Dimensional Bayesian Optimization with Group Testing
by: Hellsten, Erik, et al.
Published: (2025)
by: Hellsten, Erik, et al.
Published: (2025)
Benefits of Online Tilted Empirical Risk Minimization: A Case Study of Outlier Detection and Robust Regression
by: Yildirim, Yigit E., et al.
Published: (2025)
by: Yildirim, Yigit E., et al.
Published: (2025)
Machine Unlearning in Low-Dimensional Feature Subspace
by: Fang, Kun, et al.
Published: (2026)
by: Fang, Kun, et al.
Published: (2026)
Exploring Low-Dimensional Subspaces in Diffusion Models for Controllable Image Editing
by: Chen, Siyi, et al.
Published: (2024)
by: Chen, Siyi, et al.
Published: (2024)
Efficient Modular Learning through Naive LoRA Summation: Leveraging Orthogonality in High-Dimensional Models
by: Cao, Zhanhao, et al.
Published: (2025)
by: Cao, Zhanhao, et al.
Published: (2025)
Machine Unlearning using a Multi-GAN based Model
by: Hatua, Amartya, et al.
Published: (2024)
by: Hatua, Amartya, et al.
Published: (2024)
MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators
by: Choi, Jinyoung, et al.
Published: (2021)
by: Choi, Jinyoung, et al.
Published: (2021)
High-Dimensional Analysis of Single-Layer Attention for Sparse-Token Classification
by: Barnfield, Nicholas, et al.
Published: (2025)
by: Barnfield, Nicholas, et al.
Published: (2025)
Feature Augmentations for High-Dimensional Learning
by: Zhu, Xiaonan, et al.
Published: (2025)
by: Zhu, Xiaonan, et al.
Published: (2025)
Corner Reflector Array Jamming Discrimination Using Multi-Dimensional Micro-Motion Features with Frequency Agile Radar
by: Yuan, Jie, et al.
Published: (2026)
by: Yuan, Jie, et al.
Published: (2026)
Optimal Discriminant Analysis in High-Dimensional Latent Factor Models
by: Bing, Xin, et al.
Published: (2022)
by: Bing, Xin, et al.
Published: (2022)
A Conditional GAN for Tabular Data Generation with Probabilistic Sampling of Latent Subspaces
by: Akritidis, Leonidas, et al.
Published: (2025)
by: Akritidis, Leonidas, et al.
Published: (2025)
Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminator
by: Zheng, Kaiwen, et al.
Published: (2025)
by: Zheng, Kaiwen, et al.
Published: (2025)
High-Dimensional Tensor Discriminant Analysis with Incomplete Tensors
by: Chen, Elynn, et al.
Published: (2024)
by: Chen, Elynn, et al.
Published: (2024)
Robust Subspace-Constrained Quadratic Models for Low-Dimensional Structure Learning
by: Zhai, Zheng, et al.
Published: (2026)
by: Zhai, Zheng, et al.
Published: (2026)
Insights into Closed-form IPM-GAN Discriminator Guidance for Diffusion Modeling
by: Srikanth, Aadithya, et al.
Published: (2023)
by: Srikanth, Aadithya, et al.
Published: (2023)
High-Dimensional Bayesian Optimization via Random Projection of Manifold Subspaces
by: Nguyen, Quoc-Anh Hoang, et al.
Published: (2024)
by: Nguyen, Quoc-Anh Hoang, et al.
Published: (2024)
Exploring Multiple High-Scoring Subspaces in Generative Flow Networks
by: Yu, Xuan, et al.
Published: (2026)
by: Yu, Xuan, et al.
Published: (2026)
High-Dimensional Tensor Discriminant Analysis: Low-Rank Discriminant Structure, Representation Synergy, and Theoretical Guarantees
by: Chen, Elynn, et al.
Published: (2025)
by: Chen, Elynn, et al.
Published: (2025)
Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit
by: Zhang, Chaozhi, et al.
Published: (2024)
by: Zhang, Chaozhi, et al.
Published: (2024)
Sparse Modelling for Feature Learning in High Dimensional Data
by: Neelam, Harish, et al.
Published: (2024)
by: Neelam, Harish, et al.
Published: (2024)
Generalization Error of GAN from the Discriminator's Perspective
by: Yang, Hongkang, et al.
Published: (2021)
by: Yang, Hongkang, et al.
Published: (2021)
Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering
by: Wang, Peng, et al.
Published: (2024)
by: Wang, Peng, et al.
Published: (2024)
Orthogonal Subspace Clustering: Enhancing High-Dimensional Data Analysis through Adaptive Dimensionality Reduction and Efficient Clustering
by: Wen, Qing-Yuan, et al.
Published: (2026)
by: Wen, Qing-Yuan, et al.
Published: (2026)
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy
by: Li, Tao, et al.
Published: (2024)
by: Li, Tao, et al.
Published: (2024)
Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering
by: Xu, Jie, et al.
Published: (2021)
by: Xu, Jie, et al.
Published: (2021)
Revisiting GAN with Bayes-Optimal Discrimination
by: Naeini, Mohammadreza Tavasoli, et al.
Published: (2025)
by: Naeini, Mohammadreza Tavasoli, et al.
Published: (2025)
Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks
by: Rangriz, Parsa
Published: (2025)
by: Rangriz, Parsa
Published: (2025)
Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning
by: Zhang, Weiliang, et al.
Published: (2025)
by: Zhang, Weiliang, et al.
Published: (2025)
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers
by: Yang, Fan, et al.
Published: (2020)
by: Yang, Fan, et al.
Published: (2020)
Similar Items
-
Implicitly Normalized Online PCA: A Regularized Algorithm with Exact High-Dimensional Dynamics
by: Demir, Samet, et al.
Published: (2025) -
Learnability and Competition in High-Dimensional Multi-Component ICA
by: Genc, Eser Ilke, et al.
Published: (2026) -
Learning Rate Should Scale Inversely with High-Order Data Moments in High-Dimensional Online Independent Component Analysis
by: Gultekin, M. Oguzhan, et al.
Published: (2025) -
Optimal Attention Temperature Improves the Robustness of In-Context Learning under Distribution Shift in High Dimensions
by: Demir, Samet, et al.
Published: (2025) -
Asymptotic Analysis of Two-Layer Neural Networks after One Gradient Step under Gaussian Mixtures Data with Structure
by: Demir, Samet, et al.
Published: (2025)