Copertina

A Compositional Kernel Model for Feature Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ruan, Feng, Liu, Keli, Jordan, Michael
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A Theory of Feature Learning in Kernel Models
di: Chen, Yunlu, et al.
Pubblicazione: (2023)

A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport
di: Lin, Tianyi, et al.
Pubblicazione: (2023)

On the Uniform Convergence of Subdifferentials in Stochastic Optimization and Learning
di: Ruan, Feng
Pubblicazione: (2024)

Byzantine-Robust Distributed SGD: A Unified Analysis and Tight Error Bounds
di: Ruan, Boyuan, et al.
Pubblicazione: (2026)

The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective
di: Muehlebach, Michael, et al.
Pubblicazione: (2025)

Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization
di: Ribeiro, Antônio H., et al.
Pubblicazione: (2025)

An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints
di: Lekeufack, Jordan, et al.
Pubblicazione: (2024)

Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity
di: Zhao, Eric, et al.
Pubblicazione: (2024)

On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems
di: Muehlebach, Michael, et al.
Pubblicazione: (2021)

Perseus: A Simple and Optimal High-Order Method for Variational Inequalities
di: Lin, Tianyi, et al.
Pubblicazione: (2022)

Inertial Quadratic Majorization Minimization with Application to Kernel Regularized Learning
di: Heng, Qiang, et al.
Pubblicazione: (2025)

Kernel Mean Embedding Topology: Weak and Strong Forms for Stochastic Kernels and Implications for Model Learning
di: Saldi, Naci, et al.
Pubblicazione: (2025)

Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic
di: Zhang, Yufeng, et al.
Pubblicazione: (2021)

Offline Policy Learning with Weight Clipping and Heaviside Composite Optimization
di: Liu, Jingren, et al.
Pubblicazione: (2026)

Locally Adaptive Multi-Objective Learning
di: Kaur, Jivat Neet, et al.
Pubblicazione: (2026)

On the Limitation of Kernel Dependence Maximization for Feature Selection
di: Liu, Keli, et al.
Pubblicazione: (2024)

Scalable Kernel Inverse Optimization
di: Long, Youyuan, et al.
Pubblicazione: (2024)

Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel
di: Suwandi, Richard Cornelius, et al.
Pubblicazione: (2023)

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
di: Lin, Tianyi, et al.
Pubblicazione: (2019)

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
di: Lin, Tianyi, et al.
Pubblicazione: (2024)

Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Data
di: Qiu, Pu, et al.
Pubblicazione: (2026)

On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective
di: Westenbroek, Tyler, et al.
Pubblicazione: (2021)

Achieving Linear Speedup for Composite Federated Learning
di: Huang, Kun, et al.
Pubblicazione: (2026)

Sharper Guarantees for Misspecified Kernelized Bandit Optimization
di: Maran, Davide, et al.
Pubblicazione: (2026)

Mirror Descent on Reproducing Kernel Banach Spaces
di: Kumar, Akash, et al.
Pubblicazione: (2024)

BARK: A Fully Bayesian Tree Kernel for Black-box Optimization
di: Boyne, Toby, et al.
Pubblicazione: (2025)

Feature-Based Interpretable Surrogates for Optimization
di: Goerigk, Marc, et al.
Pubblicazione: (2024)

Bayesian Optimization by Kernel Regression and Density-based Exploration
di: Zhu, Tansheng, et al.
Pubblicazione: (2025)

Accelerating Regularized Attention Kernel Regression for Spectrum Cartography
di: Tao, Liping, et al.
Pubblicazione: (2026)

Changing the Kernel During Training Leads to Double Descent in Kernel Regression
di: Allerbo, Oskar
Pubblicazione: (2023)

A Nonparametric Approach with Marginals for Modeling Consumer Choice
di: Ruan, Yanqiu, et al.
Pubblicazione: (2022)

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning
di: Zhang, Thomas T., et al.
Pubblicazione: (2025)

Bi-Sparse Unsupervised Feature Selection
di: Xiu, Xianchao, et al.
Pubblicazione: (2024)

Faster Algorithms for Structured Linear and Kernel Support Vector Machines
di: Gu, Yuzhou, et al.
Pubblicazione: (2023)

The Stochastic Conjugate Subgradient Algorithm For Kernel Support Vector Machines
di: Zhang, Di, et al.
Pubblicazione: (2024)

Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach
di: Lengyel, Daniel, et al.
Pubblicazione: (2024)

Which Features are Best for Successor Features?
di: Ollivier, Yann
Pubblicazione: (2025)

Stochastic Compositional Optimization with Compositional Constraints
di: Yang, Shuoguang, et al.
Pubblicazione: (2022)

Going from a Representative Agent to Counterfactuals in Combinatorial Choice
di: Ruan, Yanqiu, et al.
Pubblicazione: (2025)

Shallow Neural Networks Learn Low-Degree Spherical Polynomials with Feature Learning by Learnable Channel Attention
di: Yang, Yingzhen
Pubblicazione: (2025)