Behavior Learning (BL): Learning Hierarchical Optimization Structures from Data

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References (89)

[1]
Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?
2025Maxime M'eloux, Silviu Maniu et al.
[2]
A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges
2024Guiliang Liu, Sheng Xu et al.
[3]
Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction
2024Melkamu Abay Mersha, K. Lam et al.
[4]
KAN: Kolmogorov-Arnold Networks
2024Ziming Liu, Yixuan Wang et al.
[5]
Mechanistic Interpretability for AI Safety - A Review
2024Leonard Bereska, E. Gavves
[6]
Meta Inverse Constrained Reinforcement Learning: Convergence Guarantee and Generalization Analysis
2024Shicheng Liu, Minghui Zhu
[7]
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
2023Longo Luca, Mario Brcic et al.
[8]
Interpretable generalized additive neural networks
2023Mathias Kraus, D. Tschernutter et al.
[9]
Inverse Optimization: Theory and Applications
2021T. Chan, Rafid Mahmood et al.
[10]
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
2021C. Chang, R. Caruana et al.
[11]
Physics-informed machine learning
2021G. Karniadakis, I. Kevrekidis et al.
[12]
Score-Based Generative Modeling through Stochastic Differential Equations
2020Yang Song, Jascha Narain Sohl-Dickstein et al.
[13]
Inverse Constrained Reinforcement Learning
2020Usman Anwar, Shehryar Malik et al.
[14]
Concept Bottleneck Models
2020Pang Wei Koh, Thao Nguyen et al.
[15]
Improved Techniques for Training Score-Based Generative Models
2020Yang Song, Stefano Ermon
[16]
Neural Additive Models: Interpretable Machine Learning with Neural Nets
2020Rishabh Agarwal, Nicholas Frosst et al.
[17]
Interpretable Machine Learning
2019Bradley C. Boehmke, Brandon M. Greenwell
[18]
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
2019Alejandro Barredo Arrieta, Natalia Díaz Rodríguez et al.
[19]
InterpretML: A Unified Framework for Machine Learning Interpretability
2019Harsha Nori, Samuel Jenkins et al.
[20]
TabNet: Attentive Interpretable Tabular Learning
2019Sercan Ö. Arik, Tomas Pfister

Showing 20 of 89 references

Founder's Pitch

"Develop Behavior Learning framework to enable interpretable hierarchical optimization in machine learning models integrated with behavioral science."

Interpretable Machine LearningScore: 5View PDF ↗

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2.5

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