The goal of data attribution is to trace model predictions back to training data. Despite a long
line of work towards this goal, existing approaches to data attribution tend to force users to …

Data valuation has wide use cases in machine learning, including improving data quality
and creating economic incentives for data sharing. This paper studies the robustness of data …

Good models require good training data. For overparameterized deep models, the causal
relationship between training data and model predictions is increasingly opaque and poorly …

Data valuation is a powerful framework for providing statistical insights into which data are
beneficial or detrimental to model training. Many Shapley-based data valuation methods …

Assessing the quality and impact of individual data points is critical for improving model
performance and mitigating undesirable biases within the training dataset. Several data …

Instruction tuning plays a critical role in aligning large language models (LLMs) with human
preference. Despite the vast amount of open instruction datasets, naively training a LLM on …

Data valuation, a growing field that aims at quantifying the usefulness of individual data
sources for training machine learning (ML) models, faces notable yet often overlooked …

In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a
training set to make the resulting model vulnerable to manipulation. Defending against such …

AI progress is creating a growing range of risks and opportunities, but it is often unclear how
they should be navigated. In many cases, the barriers and uncertainties faced are at least …

Data attribution seeks to trace model outputs back to training data. With the recent
development of diffusion models, data attribution has become a desired module to properly …