39 citations · 173 across the 38 of their papers we have counts for
40 papers
Distilling Diffusion Score Discrepancy for Efficient Training Data Attribution
Shixuan Liu, Joan Serrà, Kin Wai Cheuk +6
Training data attribution for diffusion models aims to identify the training samples that influence a generated instance, but existing methods either require costly per-sample grad…
A Unified Theory of Random Projection for Influence Functions
Pingbang Hu, Yuzheng Hu, Jiaqi W. Ma +1
Influence functions and related data attribution scores take the form of , where is a curvature operator. In modern overparametrized models,…
FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases
Xingjian Zhang, Sophia Moylan, Ziyang Xiong +3
Scientific knowledge bases accelerate discovery by curating findings from primary literature into structured, queryable formats for both human researchers and emerging AI systems.…
Your Reasoning Benchmark May Not Test Reasoning: Revealing Perception Bottleneck in Abstract Reasoning Benchmarks
Xinhe Wang, Jin Huang, Xingjian Zhang +2
Reasoning benchmarks such as the Abstraction and Reasoning Corpus (ARC) and ARC-AGI are widely used to assess progress in artificial intelligence and are often interpreted as probe…
Exploring Training Data Attribution under Limited Access Constraints
Shiyuan Zhang, Junwei Deng, Juhan Bae +1
Training data attribution (TDA) plays a critical role in understanding the influence of individual training data points on model predictions. Gradient-based TDA methods, popularize…
Who Gets Credit or Blame? Attributing Accountability in Modern AI Systems
Shichang Zhang, Hongzhe Du, Jiaqi W. Ma +1
Modern AI systems are typically developed through multiple stages-pretraining, fine-tuning rounds, and subsequent adaptation or alignment, where each stage builds on the previous o…
Daunce: Data Attribution through Uncertainty Estimation
Xingyuan Pan, Chenlu Ye, Joseph Melkonian +2
Training data attribution (TDA) methods aim to identify which training examples influence a model's predictions on specific test data most. By quantifying these influences, TDA sup…
Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution
Yiwen Tu, Ziqi Liu, Jiaqi W. Ma +1
Measuring task relatedness and mitigating negative transfer remain a critical open challenge in Multitask Learning (MTL). This work extends data attribution -- which quantifies the…
Taming Hyperparameter Sensitivity in Data Attribution: Practical Selection Without Costly Retraining
Weiyi Wang, Junwei Deng, Yuzheng Hu +5
Data attribution methods, which quantify the influence of individual training data points on a machine learning model, have gained increasing popularity in data-centric application…
A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning
Yuzheng Hu, Fan Wu, Haotian Ye +5
Online reinforcement learning (RL) excels in complex, safety-critical domains but suffers from sample inefficiency, training instability, and limited interpretability. Data attribu…
GraSS: Scalable Data Attribution with Gradient Sparsification and Sparse Projection
Pingbang Hu, Joseph Melkonian, Weijing Tang +2
Gradient-based data attribution methods, such as influence functions, are critical for understanding the impact of individual training samples without requiring repeated model retr…
Efficient Estimation of Shortest-Path Distance Distributions to Samples in Graphs
Alan Zhu, Jiaqi Ma, Qiaozhu Mei
As large graph datasets become increasingly common across many fields, sampling is often needed to reduce the graphs into manageable sizes. This procedure raises critical questions…
Detecting and Filtering Unsafe Training Data via Data Attribution with Denoised Representation
Yijun Pan, Taiwei Shi, Jieyu Zhao +1
Large language models (LLMs) are highly sensitive to even small amounts of unsafe training data, making effective detection and filtering essential for trustworthy model developmen…
Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities
Qirun Dai, Dylan Zhang, Jiaqi W. Ma +1
Selecting appropriate training data is crucial for instruction fine-tuning of large language models (LLMs), which aims to (1) elicit strong capabilities, and (2) achieve balanced p…
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss
Junwei Deng, Weijing Tang, Jiaqi W. Ma
Influence function, a technique rooted in robust statistics, has been adapted in modern machine learning for a novel application: data attribution -- quantifying how individual tra…
: A Library for Efficient Data Attribution
Junwei Deng, Ting-Wei Li, Shiyuan Zhang +7
Data attribution methods aim to quantify the influence of individual training samples on the prediction of artificial intelligence (AI) models. As training data plays an increasing…
Most Influential Subset Selection: Challenges, Promises, and Beyond
Yuzheng Hu, Pingbang Hu, Han Zhao +1
How can we attribute the behaviors of machine learning models to their training data? While the classic influence function sheds light on the impact of individual samples, it often…
Adversarial Attacks on Data Attribution
Xinhe Wang, Pingbang Hu, Junwei Deng +1
Data attribution aims to quantify the contribution of individual training data points to the outputs of an AI model, which has been used to measure the value of training data and c…
DCA-Bench: A Benchmark for Dataset Curation Agents
Benhao Huang, Yingzhuo Yu, Jin Huang +2
The quality of datasets plays an increasingly crucial role in the research and development of modern artificial intelligence (AI). Despite the proliferation of open dataset platfor…
Efficient Ensembles Improve Training Data Attribution
Junwei Deng, Ting-Wei Li, Shichang Zhang +1
Training data attribution (TDA) methods aim to quantify the influence of individual training data points on the model predictions, with broad applications in data-centric AI, such…
A Reliable Cryptographic Framework for Empirical Machine Unlearning Evaluation
Yiwen Tu, Pingbang Hu, Jiaqi Ma
Machine unlearning updates machine learning models to remove information from specific training samples, complying with data protection regulations that allow individuals to reques…
A Metadata-Driven Approach to Understand Graph Neural Networks
Ting Wei Li, Qiaozhu Mei, Jiaqi Ma
Graph Neural Networks (GNNs) have achieved remarkable success in various applications, but their performance can be sensitive to specific data properties of the graph datasets they…
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
Jin Huang, Xingjian Zhang, Qiaozhu Mei +1
Large language models (LLMs) are gaining increasing attention for their capability to process graphs with rich text attributes, especially in a zero-shot fashion. Recent studies de…
Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage
Catherine Huang, Chelse Swoopes, Christina Xiao +2
Machine learning models are increasingly utilized across impactful domains to predict individual outcomes. As such, many models provide algorithmic recourse to individuals who rece…