Learning structure models from complex data: principles and algorithms

现在，几乎所有的数据都以数字方式存储，拥有前所未有的计算能力来处理这些数据。这些发展创造了新的机会，通过分析存储在文本、多媒体和科学资源库中的大量复杂数据来推进计算机解释(例如自然语言处理、计算机感知)和智能数据分析(例如生物信息学)。我的研究解决了从数据中合成预测模型的根本挑战，重点是在存在潜在变量和不完全观测的情况下学习结构化预测者的问题。这些挑战的动机是，复杂领域中的预测不仅是简单的类别标签或标量值，而且是复杂的结构化输出-如分析树、场景标签或图标签--涉及以协调方式预测的多个输出，通常带有介入的潜在变量。关键问题是当一些输出或干预的潜在变量未被观察到时，训练复杂的预测者。

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.