CCF-BSF: AF: Small: Algorithms for Interactive Learning

机器学习分类器是我们日常使用的许多技术的核心组件：搜索引擎、语音识别引擎、语言翻译器、辅助驾驶系统等。这些分类器通常是通过“监督学习”的过程建立的，在这个过程中，计算机被给予一组说明期望行为的(输入、输出)对(例如，如果输入是这个英语句子，则输出应该是这个西班牙语句子)，并被告知产生一个复制这种行为的函数。这是一种僵化的学习形式，众所周知，它存在各种基本障碍；例如，有些类别的概念不能通过这种方式有效地学习。这个项目将研究如何通过转移到更自然的学习设置来克服这些障碍，在这种学习设置中，允许学习机器在学习时与人类互动，并接收比输出值更丰富的反馈。这项研究有可能影响机器学习的执行方式，并扩大其适用范围。它本质上是多学科的，因此该项目的一部分包括社区建设活动，这些活动将把不同的相关研究人员群体聚集在一起。该项目还有一个教育部分，重点是将算法和机器学习的知识带给各种学生群体，这些学生群体在STEM学科中的代表性传统上是不足的。交互式学习是一个很有前途的领域，到目前为止，该领域的大部分工作都是针对特定应用的一次性系统。这个项目的目标是给这个领域目前缺乏的部分带来严谨、形式主义和具有可证明保证的算法。这个项目的目标是形式化人类反馈的形式(对学习机器)，比传统研究更丰富的形式，例如：简单的解释(例如，这只鸟不是金丝雀，因为它的喙类型错误)；注意力集中；以及相似性判断。研究人员将设计能够使用这些反馈的算法，并在正确性和统计收敛速度方面有严格的保证。该项目特别关注于克服学习中的基本困难：学习在通常的监督框架下难以学习的概念类；学习比通常需要的例子少得多的学习；适应数据分布不断变化的情况；以及改进非监督学习的结果。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning classifiers are core components of many of the technologies we use routinely: search engines, speech recognition engines, language translators, assisted driving systems, and so on. These classifiers are typically built by a process of 'supervised learning', in which a computer is given a collection of (input, output) pairs that illustrate a desired behavior (e.g. if the input is this English sentence, the output should be this Spanish sentence) and is told to produce a function that replicates such behavior. This is a rigid form of learning that is known to suffer from a variety of fundamental hurdles; for instance, there are classes of concepts that cannot efficiently be learned in this way. This project will study how such hurdles can be overcome by moving to a more natural learning setup, in which the learning machine is allowed to interact with a human while learning, and receives feedback that is richer than just output values. This research has the potential to influence the way in which machine learning is performed and to broaden its scope of applicability. It is inherently multidisciplinary, and thus part of the project includes community-building activities that will bring together different groups of relevant researchers. There is also an educational component to the project, centered on bringing knowledge of algorithms and machine learning to various student groups that have traditionally been under-represented in STEM disciplines. Interactive learning is a field with great promise in which most of the work to date has consisted of one-off systems geared towards specific applications. This project will aim to bring rigor, formalism, and algorithms with provable guarantees to parts of this field that are currently lacking them.This project will aim to formalize forms of human feedback (to a learning machine) than are richer than those traditionally studied, such as: simple explanations (e.g. this bird is not a canary because it has the wrong type of beak); attention-focusing; and similarity judgments. The investigators will design algorithms that are able to use these kinds of feedback and have rigorous guarantees, both on correctness and on statistical rates of convergence. The project is particularly focused on overcoming fundamental hardness barriers in learning: learning concept classes that would be intractable to learn in the usual supervised framework; learning with dramatically fewer examples than would normally be needed; adapting to situations in which the distribution of the data is constantly shifting; and improving the results of unsupervised learning.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.