Cover Image

Information-Theoretic Safe Bayesian Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bottero, Alessandro G., Luis, Carlos E., Vinogradska, Julia, Berkenkamp, Felix, Peters, Jan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization
by: Luis, Carlos E., et al.
Published: (2023)

Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability
by: Luis, Carlos E., et al.
Published: (2024)

Value-Distributional Model-Based Reinforcement Learning
by: Luis, Carlos E., et al.
Published: (2023)

Safe Exploration Using Bayesian World Models and Log-Barrier Optimization
by: As, Yarden, et al.
Published: (2024)

MALIBO: Meta-learning for Likelihood-free Bayesian Optimization
by: Pan, Jiarong, et al.
Published: (2023)

Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach
by: Makonnen, Mikael, et al.
Published: (2025)

Safe Online Bid Optimization with Return on Investment and Budget Constraints
by: Castiglioni, Matteo, et al.
Published: (2022)

Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization
by: Mitra, Pallavi, et al.
Published: (2024)

Transferring Information Across Interventions in Causal Bayesian Optimization
by: Javidian, Mohammad Ali
Published: (2026)

CAPSULE: Control-Theoretic Action Perturbations for Safe Uncertainty-Aware Reinforcement Learning
by: Narava, Rahul, et al.
Published: (2026)

XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning
by: Palenicek, Daniel, et al.
Published: (2025)

Massively Scaling Explicit Policy-conditioned Value Functions
by: Bohlinger, Nico, et al.
Published: (2025)

Calibrated Test-Time Guidance for Bayesian Inference
by: Geyfman, Daniel, et al.
Published: (2026)

Online Optimization for Offline Safe Reinforcement Learning
by: Chemingui, Yassine, et al.
Published: (2025)

The Paradox of Outcome Optimization: A Causal Information-Theoretic Bound on Reasoning Shortcuts in LLMs
by: Chen, Zihan, et al.
Published: (2026)

Explainable Bayesian Optimization
by: Chakraborty, Tanmay, et al.
Published: (2024)

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing
by: Tomita, Kento, et al.
Published: (2021)

Default Machine Learning Hyperparameters Do Not Provide Informative Initialization for Bayesian Optimization
by: Prieto, Nicolás Villagrán, et al.
Published: (2026)

A Note on Bayesian Networks with Latent Root Variables
by: Zaffalon, Marco, et al.
Published: (2024)

Prime Convolutional Model: Breaking the Ground for Theoretical Explainability
by: Panelli, Francesco, et al.
Published: (2025)

Information-theoretic Bayesian Optimization: Survey and Tutorial
by: Garrido-Merchán, Eduardo C.
Published: (2025)

BI-DCGAN: A Theoretically Grounded Bayesian Framework for Efficient and Diverse GANs
by: Valizadeh, Mahsa, et al.
Published: (2025)

KTO: Model Alignment as Prospect Theoretic Optimization
by: Ethayarajh, Kawin, et al.
Published: (2024)

Poisson Process for Bayesian Optimization
by: Wang, Xiaoxing, et al.
Published: (2024)

Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation
by: Roderick, Melrose, et al.
Published: (2023)

On Information-Theoretic Measures of Predictive Uncertainty
by: Schweighofer, Kajetan, et al.
Published: (2024)

Information-Theoretic Foundations for Machine Learning
by: Jeon, Hong Jun, et al.
Published: (2024)

An Information Theoretic Approach to Machine Unlearning
by: Foster, Jack, et al.
Published: (2024)

Scaling CrossQ with Weight Normalization
by: Palenicek, Daniel, et al.
Published: (2025)

Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees
by: Marzari, Luca, et al.
Published: (2023)

Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning
by: Yao, Yihang, et al.
Published: (2023)

Prediction-Intervention Games and Invariant Sets
by: Kühne, Linus, et al.
Published: (2026)

Benchmarking the State of Networks with a Low-Cost Method Based on Reservoir Computing
by: Reimers, Felix Simon, et al.
Published: (2025)

Inversion-based Latent Bayesian Optimization
by: Chu, Jaewon, et al.
Published: (2024)

ORTHOBO: Orthogonal Bayesian Hyperparameter Optimization
by: Schröder, Maresa, et al.
Published: (2026)

An Information Theoretic Evaluation Metric For Strong Unlearning
by: Jeon, Dongjae, et al.
Published: (2024)

Information-Theoretic Foundations for Neural Scaling Laws
by: Jeon, Hong Jun, et al.
Published: (2024)

Bayesian preference elicitation for decision support in multiobjective optimization
by: Huber, Felix, et al.
Published: (2025)

SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety
by: Kim, Geon-Hyeong, et al.
Published: (2025)

Non-Myopic Multi-Objective Bayesian Optimization
by: Belakaria, Syrine, et al.
Published: (2024)