Cover Image

Hyperparameter Optimisation with Practical Interpretability and Explanation Methods in Probabilistic Curriculum Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Salt, Llewyn, Gallagher, Marcus
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Probabilistic Curriculum Learning for Goal-Based Reinforcement Learning
by: Salt, Llewyn, et al.
Published: (2025)

AlgOS: Algorithm Operating System
by: Salt, Llewyn, et al.
Published: (2025)

Combining Automated Optimisation of Hyperparameters and Reward Shape
by: Dierkes, Julian, et al.
Published: (2024)

Hyperparameter Optimization via Interacting with Probabilistic Circuits
by: Seng, Jonas, et al.
Published: (2025)

Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector
by: Toor, Aafan Ahmad, et al.
Published: (2025)

A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning
by: Adkins, Jacob, et al.
Published: (2024)

Locally-Minimal Probabilistic Explanations
by: Izza, Yacine, et al.
Published: (2023)

Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations
by: Marzari, Luca, et al.
Published: (2024)

Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models
by: Arya, Shivvrat, et al.
Published: (2024)

Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
by: Jiang, Junqi, et al.
Published: (2023)

Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
by: Stępka, Ignacy, et al.
Published: (2024)

Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
by: Wielopolski, Patryk, et al.
Published: (2024)

Trust Regions for Explanations via Black-Box Probabilistic Certification
by: Dhurandhar, Amit, et al.
Published: (2024)

A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
by: Kostrzewa, Marcin, et al.
Published: (2026)

Principled Curriculum Learning using Parameter Continuation Methods
by: Pathak, Harsh Nilesh, et al.
Published: (2025)

From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation
by: Li, Peilang, et al.
Published: (2025)

SHapley Estimated Explanation (SHEP): A Fast Post-Hoc Attribution Method for Interpreting Intelligent Fault Diagnosis
by: Chen, Qian, et al.
Published: (2025)

Faithful and Stable Neuron Explanations for Trustworthy Mechanistic Interpretability
by: Yan, Ge, et al.
Published: (2025)

Causality-Aware Local Interpretable Model-Agnostic Explanations
by: Cinquini, Martina, et al.
Published: (2022)

Explanation-Guided Adversarial Training for Robust and Interpretable Models
by: Chen, Chao, et al.
Published: (2026)

Interpreting Language Reward Models via Contrastive Explanations
by: Jiang, Junqi, et al.
Published: (2024)

GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
by: Wang, Xiaoqi, et al.
Published: (2022)

Optimisation in Neurosymbolic Learning Systems
by: van Krieken, Emile
Published: (2024)

TWIG: Towards pre-hoc Hyperparameter Optimisation and Cross-Graph Generalisation via Simulated KGE Models
by: Sardina, Jeffrey, et al.
Published: (2024)

Explanation Space: A New Perspective into Time Series Interpretability
by: Rezaei, Shahbaz, et al.
Published: (2024)

Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning
by: Giovanelli, Joseph, et al.
Published: (2023)

Probabilistic Constrained Reinforcement Learning with Formal Interpretability
by: Wang, Yanran, et al.
Published: (2023)

Understanding the Mechanisms of Fast Hyperparameter Transfer
by: Ghosh, Nikhil, et al.
Published: (2025)

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

Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
by: Ajirak, Marzieh, et al.
Published: (2025)

Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation
by: Meng, Fanyu, et al.
Published: (2024)

ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning
by: Yuan, Mingqi, et al.
Published: (2025)

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

Curriculum Abductive Learning
by: Hu, Wen-Chao, et al.
Published: (2025)

Hyperparameters in Score-Based Membership Inference Attacks
by: Pradhan, Gauri, et al.
Published: (2025)

Sequential Policy Gradient for Adaptive Hyperparameter Optimization
by: Li, Zheng, et al.
Published: (2025)

Using Large Language Models for Hyperparameter Optimization
by: Zhang, Michael R., et al.
Published: (2023)

Calibrated Dataset Condensation for Faster Hyperparameter Search
by: Ding, Mucong, et al.
Published: (2024)

Scalable Hyperparameter-Divergent Ensemble Training with Automatic Learning Rate Exploration for Large Models
by: Cheng, Hailing, et al.
Published: (2026)

Towards Verified and Targeted Explanations through Formal Methods
by: Wang, Hanchen David, et al.
Published: (2026)