:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Nakano, Shou, Liu, Yang
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2404.11874
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mathematical Foundation of Interpretable Equivariant Surrogate Models
by: Colombini, Jacopo Joy, et al.
Published: (2025)

Enhancing Model Interpretability with Local Attribution over Global Exploration
by: Zhu, Zhiyu, et al.
Published: (2024)

Surrogate Fitness Metrics for Interpretable Reinforcement Learning
by: Altmann, Philipp, et al.
Published: (2025)

Surrogate Interpretable Graph for Random Decision Forests
by: Dubey, Akshat, et al.
Published: (2025)

Beyond Linear Surrogates: High-Fidelity Local Explanations for Black-Box Models
by: Shrestha, Sanjeev, et al.
Published: (2025)

Feature Shift Localization Network
by: Barrabés, Míriam, et al.
Published: (2025)

DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization
by: Leenders, Nick, et al.
Published: (2025)

Surrogate Neural Networks Local Stability for Aircraft Predictive Maintenance
by: Ducoffe, Mélanie, et al.
Published: (2024)

Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making
by: Palar, Pramudita Satria, et al.
Published: (2026)

Gradient-Informed Temporal Sampling Improves Rollout Accuracy in PDE Surrogate Training
by: Wang, Wenshuo, et al.
Published: (2026)

Rethinking Distribution Shifts: Empirical Analysis and Inductive Modeling for Tabular Data
by: Wang, Tianyu, et al.
Published: (2023)

Multi-Level Temporal Graph Networks with Local-Global Fusion for Industrial Fault Diagnosis
by: Aryal, Bibek, et al.
Published: (2026)

SURGE: Surrogate Gradient Adaptation in Binary Neural Networks
by: Huang, Haoyu, et al.
Published: (2026)

SL-CBM: Enhancing Concept Bottleneck Models with Semantic Locality for Better Interpretability
by: Zhang, Hanwei, et al.
Published: (2026)

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

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
by: Yang, Yiyuan, et al.
Published: (2024)

SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
by: Mansur, Murat Furkan, et al.
Published: (2026)

Interpretable Hybrid-Rule Temporal Point Processes
by: Cao, Yunyang, et al.
Published: (2025)

Individualized and Interpretable Sleep Forecasting via a Two-Stage Adaptive Spatial-Temporal Model
by: Wang, Xueyi, et al.
Published: (2025)

Data Interpreter: An LLM Agent For Data Science
by: Hong, Sirui, et al.
Published: (2024)

Identifying the Best Arm in the Presence of Global Environment Shifts
by: Srisawad, Phurinut, et al.
Published: (2024)

Enhancing Data Efficiency and Feature Identification for Lithium-Ion Battery Lifespan Prediction by Deciphering Interpretation of Temporal Patterns and Cyclic Variability Using Attention-Based Models
by: Lee, Jaewook, et al.
Published: (2023)

ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
by: Chen, Hongjiang, et al.
Published: (2026)

Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation
by: Tang, Haoteng, et al.
Published: (2024)

Multiple Physics Pretraining for Physical Surrogate Models
by: McCabe, Michael, et al.
Published: (2023)

Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects
by: Gkolemis, Vasilis, et al.
Published: (2026)

Predicting Long Term Sequential Policy Value Using Softer Surrogates
by: Nam, Hyunji, et al.
Published: (2024)

State Regularized Policy Optimization on Data with Dynamics Shift
by: Xue, Zhenghai, et al.
Published: (2023)

Beyond 9-to-5: A Generative Model for Augmenting Mobility Data of Underrepresented Shift Workers
by: Ma, Haoxuan, et al.
Published: (2025)

Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition
by: Kumar, Anuj, et al.
Published: (2026)

FedNSAM:Consistency of Local and Global Flatness for Federated Learning
by: Liu, Junkang, et al.
Published: (2026)

Frequency-aware Surrogate Modeling With SMT Kernels For Advanced Data Forecasting
by: Gonel, Nicolas, et al.
Published: (2025)

AutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow
by: Liu, Jiale, et al.
Published: (2026)

Shift of Pairwise Similarities for Data Clustering
by: Chehreghani, Morteza Haghir
Published: (2021)

Enactor: From Traffic Simulators to Surrogate World Models
by: Ranjan, Yash, et al.
Published: (2026)

Understanding Fairness Surrogate Functions in Algorithmic Fairness
by: Yao, Wei, et al.
Published: (2023)

Learning to Reason: Temporal Saliency Distillation for Interpretable Knowledge Transfer
by: Dehigahawattage, Nilushika Udayangani Hewa, et al.
Published: (2026)

Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories
by: Tassopoulou, Vasiliki, et al.
Published: (2025)

From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
by: Saves, Paul, et al.
Published: (2026)

AIM: Attributing, Interpreting, Mitigating Data Unfairness
by: Liu, Zhining, et al.
Published: (2024)