:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Kreer, Philipp Alexander, Wu, Wilson, Adam, Maxwell, Furman, Zach, Hoogland, Jesse
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2509.26544
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The Loss Kernel: A Geometric Probe for Deep Learning Interpretability
by: Adam, Maxwell, et al.
Published: (2025)

Influence Dynamics and Stagewise Data Attribution
by: Lee, Jin Hwa, et al.
Published: (2025)

Differentiation and Specialization of Attention Heads via the Refined Local Learning Coefficient
by: Wang, George, et al.
Published: (2024)

From Global to Local: A Scalable Benchmark for Local Posterior Sampling
by: Hitchcock, Rohan, et al.
Published: (2025)

Revisit, Extend, and Enhance Hessian-Free Influence Functions
by: Yang, Ziao, et al.
Published: (2024)

Estimating the Local Learning Coefficient at Scale
by: Furman, Zach, et al.
Published: (2024)

Structural Inference: Interpreting Small Language Models with Susceptibilities
by: Baker, Garrett, et al.
Published: (2025)

Influence Functions for Scalable Data Attribution in Diffusion Models
by: Mlodozeniec, Bruno, et al.
Published: (2024)

Revisiting Data Attribution for Influence Functions
by: Zhu, Hongbo, et al.
Published: (2025)

Dynamics of Transient Structure in In-Context Linear Regression Transformers
by: Carroll, Liam, et al.
Published: (2025)

Better Training Data Attribution via Better Inverse Hessian-Vector Products
by: Wang, Andrew, et al.
Published: (2025)

Rescaled Influence Functions: Accurate Data Attribution in High Dimension
by: Rubinstein, Ittai, et al.
Published: (2025)

Better Hessians Matter: Studying the Impact of Curvature Approximations in Influence Functions
by: Hong, Steve, et al.
Published: (2025)

Compressibility Measures Complexity: Minimum Description Length Meets Singular Learning Theory
by: Urdshals, Einar, et al.
Published: (2025)

You Are What You Eat -- AI Alignment Requires Understanding How Data Shapes Structure and Generalisation
by: Lehalleur, Simon Pepin, et al.
Published: (2025)

A Versatile Influence Function for Data Attribution with Non-Decomposable Loss
by: Deng, Junwei, et al.
Published: (2024)

LoRIF: Low-Rank Influence Functions for Scalable Training Data Attribution
by: Li, Shuangqi, et al.
Published: (2026)

The Approximate Fisher Influence Function: Faster Estimation of Data Influence in Statistical Models
by: Lev, Omri, et al.
Published: (2024)

Distributional Training Data Attribution: What do Influence Functions Sample?
by: Mlodozeniec, Bruno, et al.
Published: (2025)

Which Data Attributes Stimulate Math and Code Reasoning? An Investigation via Influence Functions
by: Kou, Siqi, et al.
Published: (2025)

Hessian-Free Online Certified Unlearning
by: Qiao, Xinbao, et al.
Published: (2024)

Interaction-Aware Influence Functions for Group Attribution
by: Heo, Jaeseung, et al.
Published: (2026)

Integrated Influence: Data Attribution with Baseline
by: Yang, Linxiao, et al.
Published: (2025)

A Bayesian Information-Theoretic Approach to Data Attribution
by: Tailor, Dharmesh, et al.
Published: (2026)

Accumulative SGD Influence Estimation for Data Attribution
by: Shi, Yunxiao, et al.
Published: (2025)

Multitask learning with semiempirical orbital charges enables sample-efficient MLIPs
by: Neporozhnii, Ihor, et al.
Published: (2026)

Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution
by: Kowal, Matthew, et al.
Published: (2026)

Loss Landscape Degeneracy and Stagewise Development in Transformers
by: Hoogland, Jesse, et al.
Published: (2024)

Influence-based Attributions can be Manipulated
by: Yadav, Chhavi, et al.
Published: (2024)

The Local Learning Coefficient: A Singularity-Aware Complexity Measure
by: Lau, Edmund, et al.
Published: (2023)

Conformal Agent Error Attribution
by: Feng, Naihe, et al.
Published: (2026)

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation
by: Deng, Shenyang, et al.
Published: (2026)

A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning
by: Hu, Yuzheng, et al.
Published: (2025)

The H-graph with unequal masses in quantum field theory
by: Kreer, Philipp Alexander, et al.
Published: (2024)

The H-graph with equal masses in terms of multiple polylogarithms
by: Kreer, Philipp Alexander, et al.
Published: (2021)

Eliciting Latent Predictions from Transformers with the Tuned Lens
by: Belrose, Nora, et al.
Published: (2023)

Imperfect Influence, Preserved Rankings: A Theory of TRAK for Data Attribution
by: Tong, Han, et al.
Published: (2026)

Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation
by: Xie, Tong, et al.
Published: (2024)

Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods
by: Fernandez, Andres, et al.
Published: (2025)

Thermodynamic Bayesian Inference
by: Aifer, Maxwell, et al.
Published: (2024)