RI: Small: Parallel Methods for Large-Scale Probabilistic Inference

RI：小型：大规模概率推理的并行方法

• 批准号: 1421780
• 负责人: Ryan Adams
• 金额: $ 45万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421780&HistoricalAwards=false
• 关键词: RI; Small; Parallel; Methods; Large

We are undergoing a revolution in data. We have grown accustomed to constant upheaval in computing -- quicker processors, bigger storage and faster networks -- but this century presents the new challenge of almost unlimited access to raw data. Whether from sensor networks, social computing, or high-throughput cell biology, we face a deluge of data about our world. Scientists, engineers, policymakers, and industrialists need to use these enormous floods of data to make better decisions. This research project is about providing foundations for tools to achieve these goals. Simple models give only coarse understanding. The world is sophisticated and dynamic, providing rich information. Furthermore, representation of uncertainty is critical to discovering patterns in complex data. Not only are many natural processes intrinsically random, but our knowledge is always limited. The calculus of probability allows us to represent this uncertainty and design algorithms to act effectively in an unpredictable world. The gold standard for probabilistic analysis is Markov chain Monte Carlo (MCMC), a way to identify hypotheses about the unobserved structure of the world that are consistent with observed data. It is a powerful and principled way to perform data analysis, but traditional MCMC methods do not map well onto modern computing environments. MCMC is a sequential procedure that cannot generally take advantage of the parallelism offered by multi-core desktops and laptops, cloud computing, and graphical processing units. This research will develop new methods for MCMC that are provably correct, but that take advantage of large-scale parallel computing. There are a variety of broader impacts of this work. In addition to the core technical contributions, the project engages in deep scientific collaborations. New photovoltaic materials will lead to better solar cells and more sustainable energy production. New techniques for uncovering genetic regulatory mechanisms will lead to better understanding of disease. Quantitative models of mouse activity will give insight into the neural basis of behavior and provide a deeper understanding of brain disorders. From a technical point of view, this work pursues two complementary approaches to large-scale Bayesian data analysis with MCMC: 1) a novel general-purpose framework for sharing of information between parallel Markov chains for faster mixing, and 2) a new computational concept for speculative parallelization of individual Markov chains. These theoretical and practical explorations, combined with the release of associated open source software, will yield more robust and scalable probabilistic modeling. It will develop provably-correct foundations and efficient new algorithms for parallelization of Markov transition operators for posterior simulation. These operators will be used in three collaborations that are representative of the methodological demands for large-scale statistical inference: 1) predicting the efficiencies of novel organic photovoltaic materials, 2) discovering new genetic regulatory mechanisms, and 3) quantitative neuroscientific models for mouse behavior. While this proposal focuses on the generalizable technical challenges of these problems, these collaborations provide compelling examples of how machine learning can be broadly transformative.Finally, the project includes a significant outreach component, engaging with local middle schoolers, and involving underrepresented minorities in summer research.

我们正在经历一场数据革命。 我们已经习惯了计算领域的不断变化--更快的处理器、更大的存储空间和更快的网络--但这个世纪提出了新的挑战，即几乎不受限制地访问原始数据。 无论是来自传感器网络、社会计算还是高通量细胞生物学，我们都面临着关于我们世界的大量数据。 科学家、工程师、政策制定者和实业家需要利用这些海量数据来做出更好的决策。 这个研究项目是关于为实现这些目标的工具提供基础。简单的模型只能提供粗略的理解。 世界是复杂和动态的，提供了丰富的信息。 此外，不确定性的表示对于发现复杂数据中的模式至关重要。 不仅许多自然过程本质上是随机的，而且我们的知识总是有限的。 概率演算使我们能够表示这种不确定性，并设计算法，以便在不可预测的世界中有效地采取行动。概率分析的黄金标准是马尔可夫链蒙特卡罗（MCMC），这是一种识别与观测数据一致的关于世界未观测结构的假设的方法。 这是一种强大而有原则的数据分析方法，但传统的MCMC方法不能很好地映射到现代计算环境中。 MCMC是一个顺序过程，通常无法利用多核台式机和笔记本电脑、云计算和图形处理单元提供的并行性。 这项研究将开发新的MCMC方法，可证明是正确的，但利用大规模并行计算。这项工作产生了各种更广泛的影响。 除了核心技术贡献外，该项目还进行了深入的科学合作。 新的光伏材料将带来更好的太阳能电池和更可持续的能源生产。 揭示基因调控机制的新技术将使人们更好地了解疾病。小鼠活动的定量模型将深入了解行为的神经基础，并提供对大脑疾病的更深入了解。 从技术的角度来看，这项工作追求两个互补的方法，大规模贝叶斯数据分析与MCMC：1）一个新的通用框架，用于共享信息之间的并行马尔可夫链更快的混合，和2）一个新的计算概念的投机并行化的个别马尔可夫链。这些理论和实践探索，结合相关开源软件的发布，将产生更强大和可扩展的概率建模。它将为后验模拟的马尔可夫转移算子的并行化开发可证明正确的基础和有效的新算法。 这些操作员将用于三个代表大规模统计推断方法需求的合作：1）预测新型有机光伏材料的效率，2）发现新的遗传调控机制，3）小鼠行为的定量神经科学模型。虽然该提案侧重于这些问题的普遍技术挑战，但这些合作提供了令人信服的例子，说明机器学习如何广泛变革。最后，该项目包括一个重要的外展部分，与当地中学生接触，并让代表性不足的少数民族参与暑期研究。