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 …

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

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 …

We propose a general class of sample based explanations of machine learning models,
which we term generalized representers. To measure the effect of a training sample on a …

Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a
key challenge in exploiting them to our benefit. Current models based on ecological theory …

Data valuation, or the valuation of individual datum contributions, has seen growing interest
in machine learning due to its demonstrable efficacy for tasks such as noisy label detection …

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 …

Developing modern machine learning (ML) applications is data-centric, of which one
fundamental challenge is to understand the influence of data quality to ML training--" Which …

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 …