Revisiting Data Attribution for Influence Functions
Fuente:
arXiv
Guardado en:
| Autores principales: | Zhu, Hongbo, Cangelosi, Angelo |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Influence Functions for Scalable Data Attribution in Diffusion Models
por: Mlodozeniec, Bruno, et al.
Publicado: (2024)
por: Mlodozeniec, Bruno, et al.
Publicado: (2024)
Representation Understanding via Activation Maximization
por: Zhu, Hongbo, et al.
Publicado: (2025)
por: Zhu, Hongbo, et al.
Publicado: (2025)
Distributional Training Data Attribution: What do Influence Functions Sample?
por: Mlodozeniec, Bruno, et al.
Publicado: (2025)
por: Mlodozeniec, Bruno, et al.
Publicado: (2025)
Interaction-Aware Influence Functions for Group Attribution
por: Heo, Jaeseung, et al.
Publicado: (2026)
por: Heo, Jaeseung, et al.
Publicado: (2026)
Integrated Influence: Data Attribution with Baseline
por: Yang, Linxiao, et al.
Publicado: (2025)
por: Yang, Linxiao, et al.
Publicado: (2025)
Accumulative SGD Influence Estimation for Data Attribution
por: Shi, Yunxiao, et al.
Publicado: (2025)
por: Shi, Yunxiao, et al.
Publicado: (2025)
Bridging the Communication Gap: Artificial Agents Learning Sign Language through Imitation
por: Tavella, Federico, et al.
Publicado: (2024)
por: Tavella, Federico, et al.
Publicado: (2024)
Fake or Real, Can Robots Tell? Evaluating VLM Robustness to Domain Shift in Single-View Robotic Scene Understanding
por: Tavella, Federico, et al.
Publicado: (2025)
por: Tavella, Federico, et al.
Publicado: (2025)
Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Models
por: Lin, Jinxu, et al.
Publicado: (2024)
por: Lin, Jinxu, et al.
Publicado: (2024)
Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution
por: Kowal, Matthew, et al.
Publicado: (2026)
por: Kowal, Matthew, et al.
Publicado: (2026)
Influence-based Attributions can be Manipulated
por: Yadav, Chhavi, et al.
Publicado: (2024)
por: Yadav, Chhavi, et al.
Publicado: (2024)
AIM: Attributing, Interpreting, Mitigating Data Unfairness
por: Liu, Zhining, et al.
Publicado: (2024)
por: Liu, Zhining, et al.
Publicado: (2024)
The Approximate Fisher Influence Function: Faster Estimation of Data Influence in Statistical Models
por: Lev, Omri, et al.
Publicado: (2024)
por: Lev, Omri, et al.
Publicado: (2024)
Generalized Group Data Attribution
por: Ley, Dan, et al.
Publicado: (2024)
por: Ley, Dan, et al.
Publicado: (2024)
Unifying Attribution-Based Explanations Using Functional Decomposition
por: Gevaert, Arne, et al.
Publicado: (2024)
por: Gevaert, Arne, et al.
Publicado: (2024)
Efficient Ensembles Improve Training Data Attribution
por: Deng, Junwei, et al.
Publicado: (2024)
por: Deng, Junwei, et al.
Publicado: (2024)
Learning Order Forest for Qualitative-Attribute Data Clustering
por: Zhao, Mingjie, et al.
Publicado: (2026)
por: Zhao, Mingjie, et al.
Publicado: (2026)
Source Attribution for Large Language Model-Generated Data
por: Wang, Jingtan, et al.
Publicado: (2023)
por: Wang, Jingtan, et al.
Publicado: (2023)
Sparse, Efficient and Explainable Data Attribution with DualXDA
por: Yolcu, Galip Ümit, et al.
Publicado: (2024)
por: Yolcu, Galip Ümit, et al.
Publicado: (2024)
TabChange: Precise Attribute Changes in Tabular Data
por: Dahal, Arjun, et al.
Publicado: (2026)
por: Dahal, Arjun, et al.
Publicado: (2026)
Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models
por: Wu, Jiang, et al.
Publicado: (2024)
por: Wu, Jiang, et al.
Publicado: (2024)
FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentation
por: Zhu, Lin, et al.
Publicado: (2025)
por: Zhu, Lin, et al.
Publicado: (2025)
Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyond
por: Bareeva, Dilyara, et al.
Publicado: (2024)
por: Bareeva, Dilyara, et al.
Publicado: (2024)
Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering
por: Zhang, Yiqun, et al.
Publicado: (2026)
por: Zhang, Yiqun, et al.
Publicado: (2026)
Revisiting Graph Autoencoders as Implicit Contrastive Learners
por: Li, Jintang, et al.
Publicado: (2024)
por: Li, Jintang, et al.
Publicado: (2024)
Influence Functions for Preference Dataset Pruning
por: Fein, Daniel, et al.
Publicado: (2025)
por: Fein, Daniel, et al.
Publicado: (2025)
VISAT: Benchmarking Adversarial and Distribution Shift Robustness in Traffic Sign Recognition with Visual Attributes
por: Yu, Simon, et al.
Publicado: (2025)
por: Yu, Simon, et al.
Publicado: (2025)
Attributes-aware Visual Emotion Representation Learning
por: Maharjan, Rahul Singh, et al.
Publicado: (2025)
por: Maharjan, Rahul Singh, et al.
Publicado: (2025)
GraSS: Scalable Data Attribution with Gradient Sparsification and Sparse Projection
por: Hu, Pingbang, et al.
Publicado: (2025)
por: Hu, Pingbang, et al.
Publicado: (2025)
Pay Attention to What and Where? Interpretable Feature Extractor in Vision-based Deep Reinforcement Learning
por: Pham, Tien, et al.
Publicado: (2025)
por: Pham, Tien, et al.
Publicado: (2025)
Enhancing Model Interpretability with Local Attribution over Global Exploration
por: Zhu, Zhiyu, et al.
Publicado: (2024)
por: Zhu, Zhiyu, et al.
Publicado: (2024)
Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability
por: Zhang, Shichang, et al.
Publicado: (2025)
por: Zhang, Shichang, et al.
Publicado: (2025)
Who Does What in Deep Learning? Multidimensional Game-Theoretic Attribution of Function of Neural Units
por: Dixit, Shrey, et al.
Publicado: (2025)
por: Dixit, Shrey, et al.
Publicado: (2025)
2D-OOB: Attributing Data Contribution Through Joint Valuation Framework
por: Sun, Yifan, et al.
Publicado: (2024)
por: Sun, Yifan, et al.
Publicado: (2024)
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions
por: Choe, Sang Keun, et al.
Publicado: (2024)
por: Choe, Sang Keun, et al.
Publicado: (2024)
Unraveling Indirect In-Context Learning Using Influence Functions
por: Askari, Hadi, et al.
Publicado: (2025)
por: Askari, Hadi, et al.
Publicado: (2025)
Faithful and Fast Influence Function via Advanced Sampling
por: Koh, Jungyeon, et al.
Publicado: (2025)
por: Koh, Jungyeon, et al.
Publicado: (2025)
A Unified Theory of Random Projection for Influence Functions
por: Hu, Pingbang, et al.
Publicado: (2026)
por: Hu, Pingbang, et al.
Publicado: (2026)
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
por: Ma, Guozheng, et al.
Publicado: (2023)
por: Ma, Guozheng, et al.
Publicado: (2023)
Leveraging Influence Functions for Resampling Data in Physics-Informed Neural Networks
por: Naujoks, Jonas R., et al.
Publicado: (2025)
por: Naujoks, Jonas R., et al.
Publicado: (2025)
Ejemplares similares
-
Influence Functions for Scalable Data Attribution in Diffusion Models
por: Mlodozeniec, Bruno, et al.
Publicado: (2024) -
Representation Understanding via Activation Maximization
por: Zhu, Hongbo, et al.
Publicado: (2025) -
Distributional Training Data Attribution: What do Influence Functions Sample?
por: Mlodozeniec, Bruno, et al.
Publicado: (2025) -
Interaction-Aware Influence Functions for Group Attribution
por: Heo, Jaeseung, et al.
Publicado: (2026) -
Integrated Influence: Data Attribution with Baseline
por: Yang, Linxiao, et al.
Publicado: (2025)