Cover Image

Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Takhanov, Rustem, Assylbekov, Zhenisbek
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Deep Linear Discriminant Analysis Revisited
by: Tezekbayev, Maxat, et al.
Published: (2026)

Multi-layer random features and the approximation power of neural networks
by: Takhanov, Rustem
Published: (2024)

On the Intrinsic Dimensions of Data in Kernel Learning
by: Takhanov, Rustem
Published: (2026)

Gradient Descent Fails to Learn High-frequency Functions and Modular Arithmetic
by: Takhanov, Rustem, et al.
Published: (2023)

The informativeness of the gradient revisited
by: Takhanov, Rustem
Published: (2025)

On Kernel Eigen-alignments of KRR: Reconstruction and Generalization
by: Liu, Yang, et al.
Published: (2026)

Simplex Deep Linear Discriminant Analysis
by: Tezekbayev, Maxat, et al.
Published: (2026)

Learning Overspecified Gaussian Mixtures Exponentially Fast with the EM Algorithm
by: Assylbekov, Zhenisbek, et al.
Published: (2025)

A Fast Kernel-based Conditional Independence test with Application to Causal Discovery
by: Schacht, Oliver, et al.
Published: (2025)

Non-asymptotic spectral bounds on the $\varepsilon$-entropy of kernel classes
by: Takhanov, Rustem
Published: (2022)

Data-Aware Random Feature Kernel for Transformers
by: Farzam, Amirhossein, et al.
Published: (2026)

Enhancing Kernel Power K-means: Scalable and Robust Clustering with Random Fourier Features and Possibilistic Method
by: Chen, Yixi, et al.
Published: (2025)

Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independence
by: Hirose, Yushi, et al.
Published: (2026)

Conditional Clifford-Steerable CNNs with Complete Kernel Basis for PDE Modeling
by: Szarvas, Bálint László, et al.
Published: (2025)

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
by: Manten, Georg, et al.
Published: (2024)

Random Wavelet Features for Graph Kernel Machines
by: de Bassompierre, Valentin, et al.
Published: (2026)

Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels
by: Khan, Fairoz Nower, et al.
Published: (2026)

MobileKernelBench: Can LLMs Write Efficient Kernels for Mobile Devices?
by: Zou, Xingze, et al.
Published: (2026)

Amortized Linear-time Exact Shapley Value for Product-Kernel Methods
by: Mohammadi, Majid, et al.
Published: (2025)

Extending Fair Null-Space Projections for Continuous Attributes to Kernel Methods
by: Störck, Felix, et al.
Published: (2025)

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale
by: Luo, Liang, et al.
Published: (2026)

Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method
by: Tao, Qinghua, et al.
Published: (2024)

InjectTST: A Transformer Method of Injecting Global Information into Independent Channels for Long Time Series Forecasting
by: Chi, Ce, et al.
Published: (2024)

Policy Gradient with Kernel Quadrature
by: Hayakawa, Satoshi, et al.
Published: (2023)

Scalable Neural Network Kernels
by: Sehanobish, Arijit, et al.
Published: (2023)

FastKernels: Benchmarking GPU Kernel Generation in Production
by: Oliaro, Gabriele, et al.
Published: (2026)

Feature Structure Distillation with Centered Kernel Alignment in BERT Transferring
by: Jung, Hee-Jun, et al.
Published: (2022)

AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units
by: Cao, Xinzi, et al.
Published: (2026)

KernelBand: Steering LLM-based Kernel Optimization via Hardware-Aware Multi-Armed Bandits
by: Ran, Dezhi, et al.
Published: (2025)

LAPLEX: The FFT of Learnable Laplace Kernels
by: Struski, Łukasz, et al.
Published: (2026)

Transformer Neural Processes - Kernel Regression
by: Jenson, Daniel, et al.
Published: (2024)

Debiasing Kernel-Based Generative Models
by: Qin, Tian, et al.
Published: (2025)

Distributed Clustering based on Distributional Kernel
by: Zhang, Hang, et al.
Published: (2024)

HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive Modelling
by: Keller, Piotr, et al.
Published: (2024)

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization
by: Sun, Qitong, et al.
Published: (2026)

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations
by: Liu, Wei, et al.
Published: (2026)

POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspective
by: Chen, Shaowu, et al.
Published: (2023)

Omniwise: Predicting GPU Kernels Performance with LLMs
by: Wang, Zixian, et al.
Published: (2025)

A Unified Kernel for Neural Network Learning
by: Zhang, Shao-Qun, et al.
Published: (2024)

Kernel Stochastic Configuration Networks for Nonlinear Regression
by: Chen, Yongxuan, et al.
Published: (2024)