Inferring Kernel $ε$-Machines: Discovering Structure in Complex Systems
Fuente:
arXiv
Saved in:
| Main Authors: | Jurgens, Alexandra M., Brodu, Nicolas |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Discovering Causal Structure with Reproducing-Kernel Hilbert Space $ε$-Machines
by: Brodu, Nicolas, et al.
Published: (2020)
by: Brodu, Nicolas, et al.
Published: (2020)
Dimension-Independent Kernel ε-Covers
by: Phillips, Jeff M., et al.
Published: (2023)
by: Phillips, Jeff M., et al.
Published: (2023)
Predicting Dynamics of Ultra-Large Complex Systems by Inferring Governing Equations
by: Shao, Qi, et al.
Published: (2026)
by: Shao, Qi, et al.
Published: (2026)
On the $ε$-Free Inference Complexity of Absorbing Discrete Diffusion
by: Huang, Xunpeng, et al.
Published: (2025)
by: Huang, Xunpeng, et al.
Published: (2025)
Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction
by: Rakhshaninejad, Morteza, et al.
Published: (2025)
by: Rakhshaninejad, Morteza, et al.
Published: (2025)
One Class Restricted Kernel Machines
by: Quadir, A., et al.
Published: (2025)
by: Quadir, A., et al.
Published: (2025)
Laplace Approximation For Tensor Train Kernel Machines In System Identification
by: Saiapin, Albert, et al.
Published: (2025)
by: Saiapin, Albert, et al.
Published: (2025)
Twin Restricted Kernel Machines for Multiview Classification
by: Quadir, A., et al.
Published: (2025)
by: Quadir, A., et al.
Published: (2025)
Achieving $\widetilde{O}(1/ε)$ Sample Complexity for Bilinear Systems Identification under Bounded Noises
by: Yi, Hongyu, et al.
Published: (2026)
by: Yi, Hongyu, et al.
Published: (2026)
Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines
by: Milsom, Edward, et al.
Published: (2024)
by: Milsom, Edward, et al.
Published: (2024)
Convolutional Deep Kernel Machines
by: Milsom, Edward, et al.
Published: (2023)
by: Milsom, Edward, et al.
Published: (2023)
Faster Algorithms for Structured Linear and Kernel Support Vector Machines
by: Gu, Yuzhou, et al.
Published: (2023)
by: Gu, Yuzhou, et al.
Published: (2023)
Kernel $ε$-Greedy for Multi-Armed Bandits with Covariates
by: Arya, Sakshi, et al.
Published: (2023)
by: Arya, Sakshi, et al.
Published: (2023)
TRKM: Twin Restricted Kernel Machines for Classification and Regression
by: Quadir, A., et al.
Published: (2025)
by: Quadir, A., et al.
Published: (2025)
Multiple Locally Linear Kernel Machines
by: Picard, David
Published: (2024)
by: Picard, David
Published: (2024)
Notes on Kernel Methods in Machine Learning
by: Pérez-Rosero, Diego Armando, et al.
Published: (2025)
by: Pérez-Rosero, Diego Armando, et al.
Published: (2025)
On the Nystrom Approximation for Preconditioning in Kernel Machines
by: Abedsoltan, Amirhesam, et al.
Published: (2023)
by: Abedsoltan, Amirhesam, et al.
Published: (2023)
Families of Optimal Transport Kernels for Cell Complexes
by: Khorana, Rahul
Published: (2025)
by: Khorana, Rahul
Published: (2025)
Position: Epistemic uncertainty estimation methods are fundamentally incomplete
by: Jiménez, Sebastián, et al.
Published: (2025)
by: Jiménez, Sebastián, et al.
Published: (2025)
Discovering environments with XRM
by: Pezeshki, Mohammad, et al.
Published: (2023)
by: Pezeshki, Mohammad, et al.
Published: (2023)
Spectral Truncation Kernels: Noncommutativity in $C^*$-algebraic Kernel Machines
by: Hashimoto, Yuka, et al.
Published: (2024)
by: Hashimoto, Yuka, et al.
Published: (2024)
A Structure-Preserving Kernel Method for Learning Hamiltonian Systems
by: Hu, Jianyu, et al.
Published: (2024)
by: Hu, Jianyu, et al.
Published: (2024)
Analysis of Structured Deep Kernel Networks
by: Wenzel, Tizian, et al.
Published: (2021)
by: Wenzel, Tizian, et al.
Published: (2021)
Inferring Diffusion Structures of Heterogeneous Network Cascade
by: Yuan, Yubai, et al.
Published: (2025)
by: Yuan, Yubai, et al.
Published: (2025)
HH-SAE: Discovering and Steering Hierarchical Knowledge of Complex Manifolds
by: Wu, Honghan, et al.
Published: (2026)
by: Wu, Honghan, et al.
Published: (2026)
Joker: Joint Optimization Framework for Lightweight Kernel Machines
by: Zhang, Junhong, et al.
Published: (2025)
by: Zhang, Junhong, et al.
Published: (2025)
Laplace Approximation for Bayesian Tensor Network Kernel Machines
by: Saiapin, Albert, et al.
Published: (2026)
by: Saiapin, Albert, et al.
Published: (2026)
CI-RKM: A Class-Informed Approach to Robust Restricted Kernel Machines
by: Mishra, Ritik, et al.
Published: (2025)
by: Mishra, Ritik, et al.
Published: (2025)
Achieving ${O}(ε^{-1.5})$ Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization
by: Yang, Yifan, et al.
Published: (2023)
by: Yang, Yifan, et al.
Published: (2023)
Achieving $ε^{-2}$ Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions
by: Hamza, Ishaq, et al.
Published: (2026)
by: Hamza, Ishaq, et al.
Published: (2026)
Nonparametric Kernel Clustering with Bandit Feedback
by: Thuot, Victor, et al.
Published: (2026)
by: Thuot, Victor, et al.
Published: (2026)
Discovering quantum phenomena with Interpretable Machine Learning
by: de Schoulepnikoff, Paulin, et al.
Published: (2026)
by: de Schoulepnikoff, Paulin, et al.
Published: (2026)
Discovering Mixtures of Structural Causal Models from Time Series Data
by: Varambally, Sumanth, et al.
Published: (2023)
by: Varambally, Sumanth, et al.
Published: (2023)
Discovering Nuclear Models from Symbolic Machine Learning
by: Munoz, Jose M., et al.
Published: (2024)
by: Munoz, Jose M., et al.
Published: (2024)
GCL-Sampler: Discovering Kernel Similarity for Sampled GPU Simulation via Graph Contrastive Learning
by: Wang, Jiaqi, et al.
Published: (2026)
by: Wang, Jiaqi, et al.
Published: (2026)
The Exact Sample Complexity Gain from Invariances for Kernel Regression
by: Tahmasebi, Behrooz, et al.
Published: (2023)
by: Tahmasebi, Behrooz, et al.
Published: (2023)
Inferring effective couplings with Restricted Boltzmann Machines
by: Decelle, Aurélien, et al.
Published: (2023)
by: Decelle, Aurélien, et al.
Published: (2023)
Train Less, Infer Faster: Efficient Model Finetuning and Compression via Structured Sparsity
by: Svirsky, Jonathan, et al.
Published: (2026)
by: Svirsky, Jonathan, et al.
Published: (2026)
Scientific Machine Learning of Chaotic Systems Discovers Governing Equations for Neural Populations
by: Chesebro, Anthony G., et al.
Published: (2025)
by: Chesebro, Anthony G., et al.
Published: (2025)
Inferring Reward Machines and Transition Machines from Partially Observable Markov Decision Processes
by: Wu, Yuly, et al.
Published: (2025)
by: Wu, Yuly, et al.
Published: (2025)
Similar Items
-
Discovering Causal Structure with Reproducing-Kernel Hilbert Space $ε$-Machines
by: Brodu, Nicolas, et al.
Published: (2020) -
Dimension-Independent Kernel ε-Covers
by: Phillips, Jeff M., et al.
Published: (2023) -
Predicting Dynamics of Ultra-Large Complex Systems by Inferring Governing Equations
by: Shao, Qi, et al.
Published: (2026) -
On the $ε$-Free Inference Complexity of Absorbing Discrete Diffusion
by: Huang, Xunpeng, et al.
Published: (2025) -
Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction
by: Rakhshaninejad, Morteza, et al.
Published: (2025)