Cover Image

Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Qing, Jixiang, Moss, Henry, Sachs, Matthias
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

Whom to Trust? Elective Learning for Distributed Gaussian Process Regression
by: Yang, Zewen, et al.
Published: (2024)

Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
by: Belakaria, Syrine, et al.
Published: (2024)

Flow-Induced Diagonal Gaussian Processes
by: Lin, Moule, et al.
Published: (2025)

Efficient Regression-Based Training of Normalizing Flows for Boltzmann Generators
by: Rehman, Danyal, et al.
Published: (2025)

Robust Satisficing Gaussian Process Bandits Under Adversarial Attacks
by: Saday, Artun, et al.
Published: (2025)

DAO-GP Drift Aware Online Non-Linear Regression Gaussian-Process
by: Abu-Shaira, Mohammad, et al.
Published: (2025)

Hybrid Probabilistic Forecasting of Under-Five Malaria Admissions in Ghana: A Gaussian Process Regression with Holt-Winters Smoothing
by: Ansah-Narh, T., et al.
Published: (2026)

Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning
by: Rabbani, Shourav B., et al.
Published: (2023)

GPRat: Gaussian Process Regression with Asynchronous Tasks
by: Helmann, Maksim, et al.
Published: (2025)

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

Self Paced Gaussian Contextual Reinforcement Learning
by: Ardakani, Mohsen Sahraei, et al.
Published: (2026)

Improving Interpretability of Scores in Anomaly Detection Based on Gaussian-Bernoulli Restricted Boltzmann Machine
by: Sekimoto, Kaiji, et al.
Published: (2024)

Formally Verifying Analog Neural Networks Under Process Variations Using Polynomial Zonotopes
by: Abu-Haeyeh, Yasmine, et al.
Published: (2026)

Federated Active Learning Under Extreme Non-IID and Global Class Imbalance
by: Zong, Chen-Chen, et al.
Published: (2026)

Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
by: Xu, Jian, et al.
Published: (2024)

Efficient Process Reward Model Training via Active Learning
by: Duan, Keyu, et al.
Published: (2025)

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

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics
by: Niu, Tian, et al.
Published: (2024)

M-SGWR: Multiscale Similarity and Geographically Weighted Regression
by: Lessani, M. Naser, et al.
Published: (2026)

Topology-Independent Robustness of the Weighted Mean under Label Poisoning Attacks in Heterogeneous Decentralized Learning
by: Peng, Jie, et al.
Published: (2026)

Overcoming Overfitting in Reinforcement Learning via Gaussian Process Diffusion Policy
by: Horprasert, Amornyos, et al.
Published: (2025)

Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted Regression
by: Zhang, Ziqi, et al.
Published: (2024)

OLR-WA: Online Weighted Average Linear Regression in Multivariate Data Streams
by: Abu-Shaira, Mohammad, et al.
Published: (2025)

Kov: Transferable and Naturalistic Black-Box LLM Attacks using Markov Decision Processes and Tree Search
by: Moss, Robert J.
Published: (2024)

Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning
by: Xie, Shifeng, et al.
Published: (2025)

Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach
by: Shoush, Mahmoud, et al.
Published: (2023)

DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning
by: Choi, Won-Seok, et al.
Published: (2024)

Weight Concentration Regularization for Improving Pruning Robustness Under High Sparsity
by: Yun, Vincent-Daniel, et al.
Published: (2025)

Representational Alignment with Chemical Induced Fit for Molecular Relational Learning
by: Zhang, Peiliang, et al.
Published: (2025)

Gaussian Process Neural Additive Models
by: Zhang, Wei, et al.
Published: (2024)

Temporal Prototype-Aware Learning for Active Voltage Control on Power Distribution Networks
by: Xu, Feiyang, et al.
Published: (2024)

Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models
by: Goodall, Alexander W., et al.
Published: (2026)

Reference-Sampled Boltzmann Projection for KL-Regularized RLVR: Target-Matched Weighted SFT, Finite One-Shot Gaps, and Policy Mirror Descent
by: Shu, Yao, et al.
Published: (2026)

Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression
by: Shukla, Megh, et al.
Published: (2025)

Bayesian Analysis of Combinatorial Gaussian Process Bandits
by: Sandberg, Jack, et al.
Published: (2023)

Learning Dynamic Representations via An Optimally-Weighted Maximum Mean Discrepancy Optimization Framework for Continual Learning
by: Huang, KaiHui, et al.
Published: (2025)

GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs
by: Zhelnin, Maxim, et al.
Published: (2024)

Beyond Gaussian Initializations: Signal Preserving Weight Initialization for Odd-Sigmoid Activations
by: Lee, Hyunwoo, et al.
Published: (2025)

DITTO: Offline Imitation Learning with World Models
by: DeMoss, Branton, et al.
Published: (2023)

On the Laplace Approximation as Model Selection Criterion for Gaussian Processes
by: Besginow, Andreas, et al.
Published: (2024)