Partially Observable Gaussian Process Network and Doubly Stochastic Variational Inference
Fuente:
arXiv
Saved in:
| Main Authors: | Kiroriwal, Saksham, Pfrommer, Julius, Beyerer, Jürgen |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimization
by: Kiroriwal, Saksham, et al.
Published: (2026)
by: Kiroriwal, Saksham, et al.
Published: (2026)
Multi-View Causal Representation Learning with Partial Observability
by: Yao, Dingling, et al.
Published: (2023)
by: Yao, Dingling, et al.
Published: (2023)
A Sparsity Principle for Partially Observable Causal Representation Learning
by: Xu, Danru, et al.
Published: (2024)
by: Xu, Danru, et al.
Published: (2024)
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy
by: Allen, Cameron, et al.
Published: (2024)
by: Allen, Cameron, et al.
Published: (2024)
The Homogeneity Trap: Spectral Collapse in Doubly-Stochastic Deep Networks
by: Liu, Yizhi
Published: (2026)
by: Liu, Yizhi
Published: (2026)
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
by: Xu, Jian, et al.
Published: (2024)
by: Xu, Jian, et al.
Published: (2024)
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
by: Xu, Jian, et al.
Published: (2024)
by: Xu, Jian, et al.
Published: (2024)
Near-Optimal Partially Observable Reinforcement Learning with Partial Online State Information
by: Shi, Ming, et al.
Published: (2023)
by: Shi, Ming, et al.
Published: (2023)
Observation Adaptation via Annealed Importance Resampling for Partially Observable Markov Decision Processes
by: Zhang, Yunuo, et al.
Published: (2025)
by: Zhang, Yunuo, et al.
Published: (2025)
Zero-Shot Reinforcement Learning Under Partial Observability
by: Jeen, Scott, et al.
Published: (2025)
by: Jeen, Scott, et al.
Published: (2025)
Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping
by: Yuan, Jinghui, et al.
Published: (2024)
by: Yuan, Jinghui, et al.
Published: (2024)
Guided Policy Optimization under Partial Observability
by: Li, Yueheng, et al.
Published: (2025)
by: Li, Yueheng, et al.
Published: (2025)
LLMs for Text-Based Exploration and Navigation Under Partial Observability
by: Sandfuchs, Stephan, et al.
Published: (2026)
by: Sandfuchs, Stephan, et al.
Published: (2026)
An Empirical Study on the Power of Future Prediction in Partially Observable Environments
by: Kwon, Jeongyeol, et al.
Published: (2024)
by: Kwon, Jeongyeol, et al.
Published: (2024)
Temporal Knowledge-Graph Memory in a Partially Observable Environment
by: Kim, Taewoon, et al.
Published: (2024)
by: Kim, Taewoon, et al.
Published: (2024)
Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning
by: Zhang, Hongming, et al.
Published: (2023)
by: Zhang, Hongming, et al.
Published: (2023)
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)
From Shallow Bayesian Neural Networks to Gaussian Processes: General Convergence, Identifiability and Scalable Inference
by: de Araújo, Gracielle Antunes, et al.
Published: (2026)
by: de Araújo, Gracielle Antunes, et al.
Published: (2026)
Online Feedback Efficient Active Target Discovery in Partially Observable Environments
by: Sarkar, Anindya, et al.
Published: (2025)
by: Sarkar, Anindya, et al.
Published: (2025)
Recurrent Deep Reinforcement Learning for Chemotherapy Control under Partial Observability
by: Kiram, Firas Mohamed Elamine, et al.
Published: (2026)
by: Kiram, Firas Mohamed Elamine, et al.
Published: (2026)
Adversarial Latent-State Training for Robust Policies in Partially Observable Domains
by: Ahuja, Angad Singh
Published: (2026)
by: Ahuja, Angad Singh
Published: (2026)
Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning
by: Zhao, Yike, et al.
Published: (2026)
by: Zhao, Yike, et al.
Published: (2026)
Comprehensive Description of Uncertainty in Measurement for Representation and Propagation with Scalable Precision
by: Darijani, Ali, et al.
Published: (2026)
by: Darijani, Ali, et al.
Published: (2026)
Time Series Clustering with General State Space Models via Stochastic Variational Inference
by: Ishizuka, Ryoichi, et al.
Published: (2024)
by: Ishizuka, Ryoichi, et al.
Published: (2024)
Benchmarking Partial Observability in Reinforcement Learning with a Suite of Memory-Improvable Domains
by: Tao, Ruo Yu, et al.
Published: (2025)
by: Tao, Ruo Yu, et al.
Published: (2025)
Belief States for Cooperative Multi-Agent Reinforcement Learning under Partial Observability
by: Pritz, Paul J., et al.
Published: (2025)
by: Pritz, Paul J., et al.
Published: (2025)
A Convolution and Attention Based Encoder for Reinforcement Learning under Partial Observability
by: Wang, Wuhao, et al.
Published: (2025)
by: Wang, Wuhao, et al.
Published: (2025)
Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments
by: Ghanadbashi, Saeedeh, et al.
Published: (2024)
by: Ghanadbashi, Saeedeh, et al.
Published: (2024)
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games
by: Altabaa, Awni, et al.
Published: (2024)
by: Altabaa, Awni, et al.
Published: (2024)
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
by: Xu, Jian, et al.
Published: (2024)
by: Xu, Jian, et al.
Published: (2024)
Transformer-Based Reinforcement Learning for Autonomous Orbital Collision Avoidance in Partially Observable Environments
by: Georges, Thomas, et al.
Published: (2026)
by: Georges, Thomas, et al.
Published: (2026)
Learning Interpretable Policies in Hindsight-Observable POMDPs through Partially Supervised Reinforcement Learning
by: Lanier, Michael, et al.
Published: (2024)
by: Lanier, Michael, et al.
Published: (2024)
Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability
by: Kim, Taewoon, et al.
Published: (2026)
by: Kim, Taewoon, et al.
Published: (2026)
Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability
by: Luis, Carlos E., et al.
Published: (2024)
by: Luis, Carlos E., et al.
Published: (2024)
Learning to Focus: Prioritizing Informative Histories with Structured Attention Mechanisms in Partially Observable Reinforcement Learning
by: Allegue, Daniel De Dios, et al.
Published: (2025)
by: Allegue, Daniel De Dios, et al.
Published: (2025)
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
by: Lu, Miao, et al.
Published: (2022)
by: Lu, Miao, et al.
Published: (2022)
Derivation of Output Correlation Inferences for Multi-Output (aka Multi-Task) Gaussian Process
by: Watanabe, Shuhei
Published: (2025)
by: Watanabe, Shuhei
Published: (2025)
When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback
by: Lang, Leon, et al.
Published: (2024)
by: Lang, Leon, et al.
Published: (2024)
Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learning
by: Bajaj, Chandrajit, et al.
Published: (2024)
by: Bajaj, Chandrajit, et al.
Published: (2024)
Doubly Inhomogeneous Reinforcement Learning
by: Hu, Liyuan, et al.
Published: (2022)
by: Hu, Liyuan, et al.
Published: (2022)
Similar Items
-
Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimization
by: Kiroriwal, Saksham, et al.
Published: (2026) -
Multi-View Causal Representation Learning with Partial Observability
by: Yao, Dingling, et al.
Published: (2023) -
A Sparsity Principle for Partially Observable Causal Representation Learning
by: Xu, Danru, et al.
Published: (2024) -
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy
by: Allen, Cameron, et al.
Published: (2024) -
The Homogeneity Trap: Spectral Collapse in Doubly-Stochastic Deep Networks
by: Liu, Yizhi
Published: (2026)