Learning structure models from complex data: principles and algorithms

现在几乎所有的数据都是数字化存储的，拥有前所未有的计算能力来处理这些数据。这些发展通过分析存储在文本、多媒体和科学知识库中的大量复杂数据，为推进计算机解释（如自然语言处理、计算机感知）和智能数据分析（如生物信息学）创造了新的机会。我的研究解决了从数据中合成预测模型的基本挑战，重点是在潜在变量和不完整观察存在的情况下学习结构化预测器的问题。这些挑战的动机是这样一个事实：复杂领域的预测不仅仅是简单的类标签或标量值，而是复杂的结构化输出——比如解析树、场景标签或图形标签——涉及以协调的方式预测的多个输出，通常带有干预的潜在变量。关键问题是训练复杂的预测器时，一些输出或干预潜在变量是未观察到的。

Almost all data is now digitally stored, with unprecedented computing power available to process it. These developments have created new opportunities to advance computer interpretation (e.g. natural language processing, computer perception) and intelligent data-analysis (e.g. bio-informatics) by analyzing the massive amounts of complex data stored in text, multimedia, and scientific repositories. My research addresses the fundamental challenge of synthesizing predictive models from data, focusing on the problem of learning structured predictors in the presence of latent variables and incomplete observations. These challenges are motivated by the fact that predictions in complex domains are not just simple class labels or scalar values, but are complex structured outputs---such as parse trees, scene labellings or graph labellings---that involve multiple outputs to be predicted in a coordinated fashion, often with intervening latent variables. The key problem is training complex predictors when some of the output or intervening latent variables are unobserved.