Toniann Pitassi

Theoretical bounds on estimation error for meta-learning

2020 · arXiv (Cornell University)

Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning and related problems are encouraging signs that these models can be …

Reproducibility in Learning

2022 · arXiv (Cornell University)

We introduce the notion of a reproducible algorithm in the context of learning. A reproducible learning algorithm is resilient to variations in its samples -- with high probability, it returns the exact same output when …

Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models

2023 · arXiv (Cornell University)

The recent explosion in the capabilities of large language models has led to a wave of interest in how best to prompt a model to perform a given task. While it may be tempting to …