Learning from Summarized Data: Gaussian Process Regression with Sample Quasi-Likelihood
Fuente:
arXiv
Saved in:
| Main Author: | Shikuri, Yuta |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Likelihood-Free Gaussian Process for Regression
by: Shikuri, Yuta
Published: (2020)
by: Shikuri, Yuta
Published: (2020)
Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network
by: Shikuri, Yuta
Published: (2020)
by: Shikuri, Yuta
Published: (2020)
Learning Survival Models with Right-Censored Reporting Delays
by: Shikuri, Yuta, et al.
Published: (2025)
by: Shikuri, Yuta, et al.
Published: (2025)
Surrogate Graph Partitioning for Spatial Prediction
by: Shikuri, Yuta, et al.
Published: (2025)
by: Shikuri, Yuta, et al.
Published: (2025)
Mixed Likelihood Variational Gaussian Processes
by: Wu, Kaiwen, et al.
Published: (2025)
by: Wu, Kaiwen, et al.
Published: (2025)
Algorithms and Scientific Software for Quasi-Monte Carlo, Fast Gaussian Process Regression, and Scientific Machine Learning
by: Sorokin, Aleksei G.
Published: (2025)
by: Sorokin, Aleksei G.
Published: (2025)
Distributionally Robust Active Learning for Gaussian Process Regression
by: Takeno, Shion, et al.
Published: (2025)
by: Takeno, Shion, et al.
Published: (2025)
Reward Redistribution via Gaussian Process Likelihood Estimation
by: Xiao, Minheng, et al.
Published: (2025)
by: Xiao, Minheng, et al.
Published: (2025)
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes
by: Lin, Jihao Andreas, et al.
Published: (2024)
by: Lin, Jihao Andreas, et al.
Published: (2024)
Phased Data Augmentation for Training a Likelihood-Based Generative Model with Limited Data
by: Mimura, Yuta
Published: (2023)
by: Mimura, Yuta
Published: (2023)
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes
by: Hsin, Junette, et al.
Published: (2023)
by: Hsin, Junette, et al.
Published: (2023)
Batch Active Learning in Gaussian Process Regression using Derivatives
by: Yu, Hon Sum Alec, et al.
Published: (2024)
by: Yu, Hon Sum Alec, et al.
Published: (2024)
Implicit Manifold Gaussian Process Regression
by: Fichera, Bernardo, et al.
Published: (2023)
by: Fichera, Bernardo, et al.
Published: (2023)
Robust and Conjugate Gaussian Process Regression
by: Altamirano, Matias, et al.
Published: (2023)
by: Altamirano, Matias, et al.
Published: (2023)
Active Learning for Manifold Gaussian Process Regression
by: Cheng, Yuanxing, et al.
Published: (2025)
by: Cheng, Yuanxing, et al.
Published: (2025)
Bayesian Circular Regression with von Mises Quasi-Processes
by: Cohen, Yarden, et al.
Published: (2024)
by: Cohen, Yarden, et al.
Published: (2024)
Obstacle-aware Gaussian Process Regression
by: Shrivastava, Gaurav
Published: (2024)
by: Shrivastava, Gaurav
Published: (2024)
An Intuitive Tutorial to Gaussian Process Regression
by: Wang, Jie
Published: (2020)
by: Wang, Jie
Published: (2020)
Relative Information Gain and Gaussian Process Regression
by: Flynn, Hamish
Published: (2025)
by: Flynn, Hamish
Published: (2025)
Approximately Unimodal Likelihood Models for Ordinal Regression
by: Yamasaki, Ryoya
Published: (2025)
by: Yamasaki, Ryoya
Published: (2025)
Quasi-Periodic Gaussian Process Predictive Iterative Learning Control
by: Nigam, Unnati, et al.
Published: (2026)
by: Nigam, Unnati, et al.
Published: (2026)
Whom to Trust? Elective Learning for Distributed Gaussian Process Regression
by: Yang, Zewen, et al.
Published: (2024)
by: Yang, Zewen, et al.
Published: (2024)
GPgym: A Remote Service Platform with Gaussian Process Regression for Online Learning
by: Dai, Xiaobing, et al.
Published: (2024)
by: Dai, Xiaobing, et al.
Published: (2024)
Privacy-aware Gaussian Process Regression
by: Tuo, Rui, et al.
Published: (2023)
by: Tuo, Rui, et al.
Published: (2023)
Sparse Techniques for Regression in Deep Gaussian Processes
by: Latz, Jonas, et al.
Published: (2025)
by: Latz, Jonas, et al.
Published: (2025)
Scaling Gaussian Process Regression with Full Derivative Observations
by: Huang, Daniel
Published: (2025)
by: Huang, Daniel
Published: (2025)
Guaranteed Coverage Prediction Intervals with Gaussian Process Regression
by: Papadopoulos, Harris
Published: (2023)
by: Papadopoulos, Harris
Published: (2023)
Variational Bayesian Methods for a Tree-Structured Stick-Breaking Process Mixture of Gaussians by Application of the Bayes Codes for Context Tree Models
by: Nakahara, Yuta
Published: (2024)
by: Nakahara, Yuta
Published: (2024)
Assessing Quantum Advantage for Gaussian Process Regression
by: Lowe, Dominic, et al.
Published: (2025)
by: Lowe, Dominic, et al.
Published: (2025)
Quantum Gaussian Process Regression for Bayesian Optimization
by: Rapp, Frederic, et al.
Published: (2023)
by: Rapp, Frederic, et al.
Published: (2023)
High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation
by: Camaño, Chris, et al.
Published: (2024)
by: Camaño, Chris, et al.
Published: (2024)
Review of Recent Advances in Gaussian Process Regression Methods
by: Lyu, Chenyi, et al.
Published: (2024)
by: Lyu, Chenyi, et al.
Published: (2024)
Likelihood approximations via Gaussian approximate inference
by: Bui, Thang D.
Published: (2024)
by: Bui, Thang D.
Published: (2024)
Integrated Data Analysis of Plasma Electron Density Profile Tomography for HL-3 with Gaussian Process Regression
by: Wang, Cong, et al.
Published: (2025)
by: Wang, Cong, et al.
Published: (2025)
Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations
by: Li, Chengkun, et al.
Published: (2025)
by: Li, Chengkun, et al.
Published: (2025)
Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights
by: Qing, Jixiang, et al.
Published: (2026)
by: Qing, Jixiang, et al.
Published: (2026)
Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods
by: Walchessen, Julia, et al.
Published: (2023)
by: Walchessen, Julia, et al.
Published: (2023)
Modal Analysis of Spatiotemporal Data via Multivariate Gaussian Process Regression
by: Song, Jiwoo, et al.
Published: (2024)
by: Song, Jiwoo, et al.
Published: (2024)
Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images
by: Ong, D. Chee Yong, et al.
Published: (2025)
by: Ong, D. Chee Yong, et al.
Published: (2025)
Intrinsic Gaussian Process Regression Modeling for Manifold-valued Response Variable
by: Wang, Zhanfeng, et al.
Published: (2024)
by: Wang, Zhanfeng, et al.
Published: (2024)
Similar Items
-
Likelihood-Free Gaussian Process for Regression
by: Shikuri, Yuta
Published: (2020) -
Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network
by: Shikuri, Yuta
Published: (2020) -
Learning Survival Models with Right-Censored Reporting Delays
by: Shikuri, Yuta, et al.
Published: (2025) -
Surrogate Graph Partitioning for Spatial Prediction
by: Shikuri, Yuta, et al.
Published: (2025) -
Mixed Likelihood Variational Gaussian Processes
by: Wu, Kaiwen, et al.
Published: (2025)