Cover Image

Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Miroszewski, Artur
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods
by: Miroszewski, Artur, et al.
Published: (2024)

Entanglement Detection with Quantum-inspired Kernels and SVMs
by: Martínez-Sabiote, Ana, et al.
Published: (2025)

New Equivalences Between Interpolation and SVMs: Kernels and Structured Features
by: Kaushik, Chiraag, et al.
Published: (2023)

Learning in Prophet Inequalities with Noisy Observations
by: Kim, Jung-hun, et al.
Published: (2026)

RPN: Reconciled Polynomial Network Towards Unifying PGMs, Kernel SVMs, MLP and KAN
by: Zhang, Jiawei
Published: (2024)

Convex Loss Functions for Support Vector Machines (SVMs) and Neural Networks
by: Portera, Filippo
Published: (2026)

$p$SVM: Soft-margin SVMs with $p$-norm Hinge Loss
by: Sun, Haoxiang
Published: (2024)

Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning
by: Ordóñez, Sebastián Andrés Cajas, et al.
Published: (2025)

Local Binary and Multiclass SVMs Trained on a Quantum Annealer
by: Zardini, Enrico, et al.
Published: (2024)

Late Breaking Results: Energy-Efficient Printed Machine Learning Classifiers with Sequential SVMs
by: Besias, Spyridon, et al.
Published: (2025)

AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks
by: Zhang, Zhongyue, et al.
Published: (2026)

Learning Treatment Allocations with Risk Control Under Partial Identifiability
by: Ek, Sofia, et al.
Published: (2025)

Node Splitting SVMs for Survival Trees Based on an L2-Regularized Dipole Splitting Criteria
by: Maung, Aye Aye, et al.
Published: (2025)

ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection
by: Lastrucci, Giacomo, et al.
Published: (2025)

Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures
by: Zhang, Mingyuan, et al.
Published: (2024)

Probabilistic Machine Learning for Noisy Labels in Earth Observation
by: Kondylatos, Spyros, et al.
Published: (2025)

On the Consistency of Kernel Methods with Dependent Observations
by: Massiani, Pierre-François, et al.
Published: (2024)

Local Graph Clustering with Noisy Labels
by: de Luca, Artur Back, et al.
Published: (2023)

Foster Adaptivity and Balance in Learning with Noisy Labels
by: Sheng, Mengmeng, et al.
Published: (2024)

Optimal Transport for Measures with Noisy Tree Metric
by: Le, Tam, et al.
Published: (2023)

Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal
by: Sedova, Anastasiia, et al.
Published: (2023)

Learning to Approximate Adaptive Kernel Convolution on Graphs
by: Sim, Jaeyoon, et al.
Published: (2024)

State Estimation Using Particle Filtering in Adaptive Machine Learning Methods: Integrating Q-Learning and NEAT Algorithms with Noisy Radar Measurements
by: Song, Wonjin, et al.
Published: (2025)

Weighted Spectral Filters for Kernel Interpolation on Spheres: Estimates of Prediction Accuracy for Noisy Data
by: Liu, Xiaotong, et al.
Published: (2024)

Learning Disease-Sensitive Latent Interaction Graphs From Noisy Cardiac Flow Measurements
by: Patel, Viraj, et al.
Published: (2026)

Adaptive Kernel Selection for Kernelized Diffusion Maps
by: Aboussaad, Othmane, et al.
Published: (2026)

Epistemic Uncertainty and Observation Noise with the Neural Tangent Kernel
by: Calvo-Ordoñez, Sergio, et al.
Published: (2024)

The Generalised Kernel Covariance Measure
by: Bergen, Luca, et al.
Published: (2026)

How Well Can Preference Optimization Generalize Under Noisy Feedback?
by: Im, Shawn, et al.
Published: (2025)

LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training
by: Moturu, Abhishek, et al.
Published: (2025)

Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes
by: Jia, Su, et al.
Published: (2023)

Adaptive Budget Allocation in LLM-Augmented Surveys
by: Ye, Zikun, et al.
Published: (2026)

Adaptive Policy Learning Under Unknown Network Interference
by: Gleich, Aidan, et al.
Published: (2026)

Kernel Representation and Similarity Measure for Incomplete Data
by: Cao, Yang, et al.
Published: (2025)

AKBR: Learning Adaptive Kernel-based Representations for Graph Classification
by: Qian, Feifei, et al.
Published: (2024)

Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning
by: He, Fan, et al.
Published: (2024)

Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
by: Andersson, William, et al.
Published: (2023)

Generalization in Kernel Regression Under Realistic Assumptions
by: Barzilai, Daniel, et al.
Published: (2023)

Knowledge-Informed Kernel State Reconstruction from Heterogeneous Partial Observations
by: Muscarnera, Luca, et al.
Published: (2026)

Active Learning for Graphs with Noisy Structures
by: Chi, Hongliang, et al.
Published: (2024)