AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …

Abstract Machine unlearning is an emerging task of removing the influence of selected
training datapoints from a trained model upon data deletion requests, which echoes the …

With evolving data regulations, machine unlearning (MU) has become an important tool for
fostering trust and safety in today's AI models. However, existing MU methods focusing on …

Large Language Models (LLMs) bring transformative benefits alongside unique challenges,
including intellectual property (IP) and ethical concerns. This position paper explores a …

Quantifying the impact of training data points is crucial for understanding the outputs of
machine learning models and for improving the transparency of the AI pipeline. The …

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 …

Generative AI models perturb the foundations of effective human communication. They
present new challenges to contextual confidence, disrupting participants' ability to identify …

Indirect experiments provide a valuable framework for estimating treatment effects in
situations where conducting randomized control trials (RCTs) is impractical or unethical …

Model merging aims to cheaply combine individual task-specific models into a single
multitask model. In this work, we view past merging methods as leveraging different notions …

Second-order methods for deep learning--such as KFAC--can be useful for neural net
training. However, they are often memory-inefficient and numerically unstable for low …