:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wu, Min, Li, Xiaofu, Wu, Haoze, Barrett, Clark
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2409.03060
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Lemur: Integrating Large Language Models in Automated Program Verification
by: Wu, Haoze, et al.
Published: (2023)

Formal Synthesis of Certifiably Robust Neural Lyapunov-Barrier Certificates
by: Wang, Chengxiao, et al.
Published: (2026)

Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
by: Wu, Haoze, et al.
Published: (2024)

Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates
by: Mandal, Udayan, et al.
Published: (2024)

Fast Adaptive Anti-Jamming Channel Access via Deep Q Learning and Coarse-Grained Spectrum Prediction
by: Zhang, Jianshu, et al.
Published: (2025)

The Luna Bound Propagator for Formal Analysis of Neural Networks
by: LeCates, Henry, et al.
Published: (2026)

Even-if Explanations: Formal Foundations, Priorities and Complexity
by: Alfano, Gianvincenzo, et al.
Published: (2024)

The Fifth International Verification of Neural Networks Competition (VNN-COMP 2024): Summary and Results
by: Brix, Christopher, et al.
Published: (2024)

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

FAME: Formal Abstract Minimal Explanation for Neural Networks
by: Boumazouza, Ryma, et al.
Published: (2026)

Extending XReason: Formal Explanations for Adversarial Detection
by: Jemaa, Amira, et al.
Published: (2024)

Formal Explanations for Neuro-Symbolic AI
by: Paul, Sushmita, et al.
Published: (2024)

GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory
by: Wu, Haoze, et al.
Published: (2024)

FLEX: Feature Importance from Layered Counterfactual Explanations
by: Keshtmand, Nawid, et al.
Published: (2025)

On the Hardness of Computing Counterfactual and Semifactual Explanations in XAI
by: Artelt, André, et al.
Published: (2026)

Generally-Occurring Model Change for Robust Counterfactual Explanations
by: Xu, Ao, et al.
Published: (2024)

Clover: Closed-Loop Verifiable Code Generation
by: Sun, Chuyue, et al.
Published: (2023)

CoScale-RL: Efficient Post-Training by Co-Scaling Data and Computation
by: Chen, Yutong, et al.
Published: (2026)

Automated Conjecture Resolution with Formal Verification
by: Ju, Haocheng, et al.
Published: (2026)

Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries
by: Li, Zhihao, et al.
Published: (2024)

CauchyNet: Compact and Data-Efficient Learning using Holomorphic Activation Functions
by: Zhang, Hong-Kun, et al.
Published: (2025)

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

How Causal Abstraction Underpins Computational Explanation
by: Geiger, Atticus, et al.
Published: (2025)

A Compact LSTM-SVM Fusion Model for Long-Duration Cardiovascular Diseases Detection
by: Wu, Siyang
Published: (2023)

Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models
by: Löfström, Tuwe, et al.
Published: (2024)

LiteGUI: Distilling Compact GUI Agents with Reinforcement Learning
by: Wu, Yubin, et al.
Published: (2026)

ReCode: Updating Code API Knowledge with Reinforcement Learning
by: Wu, Haoze, et al.
Published: (2025)

TuckA: Hierarchical Compact Tensor Experts for Efficient Fine-Tuning
by: Lei, Qifeng, et al.
Published: (2025)

E-TCAV: Formalizing Penultimate Proxies for Efficient Concept Based Interpretability
by: Aslam, Hasib, et al.
Published: (2026)

Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates
by: Song, Haoze, et al.
Published: (2026)

EmbedLLM: Learning Compact Representations of Large Language Models
by: Zhuang, Richard, et al.
Published: (2024)

FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models
by: Yu, Zhouliang, et al.
Published: (2025)

A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanations
by: Artelt, André, et al.
Published: (2024)

Explanations are a Means to an End: Decision Theoretic Explanation Evaluation
by: Guo, Ziyang, et al.
Published: (2025)

Hierarchical Structure-Property Alignment for Data-Efficient Molecular Generation and Editing
by: Fan, Ziyu, et al.
Published: (2025)

Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
by: Zhang, Jiaxing, et al.
Published: (2025)

Delivering Inflated Explanations
by: Izza, Yacine, et al.
Published: (2023)

Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis
by: Wei, Anjiang, et al.
Published: (2025)

Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection
by: Chakraborty, Tanmay, et al.
Published: (2025)

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference
by: Li, Bo, et al.
Published: (2026)