CAREER: Theoretical Foundations of Modern Machine Learning Paradigms: Generative and Out-of-Distribution

在过去的几年里，大规模机器学习模型的性能有了显著的提高。像DALL-E这样的模型，经过大量文本和图像数据的训练，只需给出一个口头描述，就可以生成高质量的图像（“提示”）的图像的内容，即使是提示是非常远离什么模型的训练。像ChatGPT模型，训练了大量的文本数据和与人类的互动，能够令人信服地表现得像一个人类对话者，甚至解决简单的数学考试问题。虽然这些系统代表了令人印象深刻的工程壮举，但我们对训练它们的配方中哪些成分是重要的科学理解严重缺乏。因此，改进它们通常涉及大量的试验和错误，这也转化为大量的人力和计算资源。我们对它们的故障模式的理解（或者甚至是如何评估它们！）更穷。因此，目前不太可能在任何安全关键的情况下部署它们。该项目的目标是为理解现代机器学习模型的故障模式建立科学和数学基础，特别是在它们训练和部署的数据之间存在变化的情况下。 该项目将概述在建立数学和科学基础以理解和改进现代生成模型方面的挑战，以及正式化可以处理与训练分布大不相同的分布的设置。研究者将建立有用的学习范式的形式化，使它们易于处理的结构假设，并为这种设置开发新的算法工具。该项目将有两个主要方面：（1）在概率生成模型的背景下进行采样，推理和学习的数学工具，以及开发用于理解不同模型家族的故障模式的分析机制和规避和改善它们的算法解决方案。(2)涉及数据分布变化的学习设置，包括领域泛化（当呈现来自多个数据环境的数据，并且期望学习者在新环境中表现良好时），域翻译（当一个模型呈现来自两个领域的数据，并期望学习如何在它们之间“翻译”时）和持续学习（当来自不同环境的数据以在线方式呈现时，学习者在所有环境中都能保持良好的表现）该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

The performance of large-scale machine learning models in the last several years has improved dramatically. Models like DALL-E, trained on vast amounts of text and image data, can generate high-quality images given just a verbal description ("prompt") of the image's content, even for prompts that are very far from what the model was trained on. Models like ChatGPT, trained on vast amounts of text data and interactions with humans, are capable of convincingly behaving like a human interlocutor, even solving simple mathematics exam questions. Though these systems represent an impressive feat of engineering, our scientific understanding of what ingredients in the recipe to train them are important is severely lacking. Thus, improving them often involves extensive trial and error, which also translates to considerable amounts of human hours and computational resources. Our understanding of their failure modes (or often even how to evaluate them!) is even poorer. Thus, deploying them in any safety-critical scenario is, at present, unlikely. The goal of this project is to build scientific and mathematical foundations for understanding failure modes of modern machine learning models, especially in the presence of changes between the data they are trained on and deployed on. This project will outline the challenges in building mathematical and scientific footing for understanding and improving modern generative models, as well as formalize settings in which distributions substantially different from the training distribution can be handled. The investigator will establish useful formalizations of learning paradigms, structural assumptions that make them tractable, and develop new algorithmic tools for such settings. The project will have two major prongs: (1) Algorithmic tools for sampling, inference, and learning in the context of probabilistic generative models, as well as developing analytical machinery for understanding failure modes of different families of models and algorithmic solutions to circumvent and ameliorate them. (2) Learning settings involving shifts in the data distribution, including domain generalization (when data from several data environments is presented, and the learner is expected to perform well in a new environment), domain translation (when a model is presented data from two domains and is expected to learn how to "translate" between them) and continual learning (when data from different environments is presented in an online fashion, and the learner is expected to retain good performance in all environments).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.