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 …

Federated Learning (FL) enables collaborative model training while preserving the privacy
of raw data. A challenge in this framework is the fair and efficient valuation of data which is …

As Machine Learning (ML) systems continue to grow, the demand for relevant and
comprehensive datasets becomes imperative. There is limited study on the challenges of …

Data is the new oil of the 21st century. The growing trend of trading data for greater welfare
has led to the emergence of data markets. A data market is any mechanism whereby the …

Domain reweighting is an emerging research area aimed at adjusting the relative weights of
different data sources to improve the effectiveness and efficiency of language model pre …

Artificial intelligence (AI) model services offer remarkable efficiency and automation,
engaging customers across various tasks. However, not all AI consumers possess sufficient …

We propose TAROT, a targeted data selection framework grounded in optimal transport
theory. Previous targeted data selection methods primarily rely on influence-based greedy …

Machine learning (ML) models often require large amounts of data to perform well. When the
available data is limited, model trainers may need to acquire more data from external …

In many predictive contexts (eg, credit lending), true outcomes are only observed for
samples that were positively classified in the past. These past observations, in turn, form …

Wasserstein distance is a principle measure of data divergence from a distributional
standpoint. However, its application becomes challenging in the context of data privacy …