Collaborative Research: Theoretical and Algorithmic Foundations of Variational Bayesian Inference

数据采集​​、处理和存储技术的惊人进步为现代统计学家提供了独特的机会来分析在许多科学研究以及社会和经济领域的研究中出现的前所未有的丰富的大型复杂数据集。 贝叶斯推理将先验知识和数据信息组合成后验分布，为复杂多级数据集的概率建模以及以有原则的方式执行相关的推理或预测任务提供了一种流行的范例。对于大多数实际问题，计算后验概率需要数值近似；为此，基于采样的方法（例如马尔可夫链蒙特卡罗和确定性近似）都受到了广泛的关注。在基于优化的确定性方法中，变分近似（通常也称为变分推理）由于其对大型数据集的可扩展性而非常流行。通过这个项目，研究人员将探索流行变分过程的统计和算法特性，并开发基于强大理论基础的新方法和计算工具。结果旨在使从业者能够更好地理解变分推理可能成功的情况以及存在潜在陷阱的情况。该研究将通过知名媒体的文章和演讲进行传播。此外，所开发方法的软件包将公开提供。研究人员致力于通过为学生提供建议以及在各自机构开发研究生和本科生主题课程来增强该提案的教学部分。由于缓解贝叶斯计算中的可扩展性问题的需求日益增长，变分推理作为一种近似贝叶斯计算技术在过去二十年中得到了极大的普及。尽管变分推理在大型复杂数据领域中取得了实证上的成功，但对其统计特性的系统研究最近才开始。通过这个项目，研究人员将提出许多基本问题，以解决理解和解释变分近似在参数估计、统计推断和模型选择方面取得的巨大经验成功以及在新领域中的应用的理论挑战。研究人员还将开发通用充分条件来证明常用变分算法的收敛性。理论发展将采用动力系统、功能优化和最优传输等工具，从而统一处理变分推理的统计和算法方面。根据这一新理论，研究人员将对现有算法提出修改，以确保具有更好的收敛行为。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影响审查标准进行评估，被认为值得支持。

Spectacular advances in data acquisition, processing and storage techniques offer modern-day statisticians a unique opportunity to analyze large and complex datasets of unprecedented richness which arise in many scientific investigations and in studies in the social and economic fields. Bayesian inference, which combines prior knowledge and data information into a posterior distribution, provides a popular paradigm for probabilistic modeling of complex multi-level datasets and for performing associated inferential or predictive tasks in a principled fashion. For most practical problems, computing the posterior probabilities require numerical approximations; to that end, sampling-based approaches such as Markov chain Monte Carlo and deterministic approximations have both received widespread attention. Among deterministic approaches based on optimization, variational approximations, also commonly referred to as variational inference, is highly popular due to its scalability to large datasets. Through this project, the investigators will explore statistical and algorithmic properties of popular variational procedures and develop new methodology and computational tools grounded on a strong theoretical foundation. The results are targeted to empower practitioners with a better understanding of situations where variational inference is likely to be successful and where potential pitfalls exist. The research will be disseminated through articles and talks at prominent outlets. Additionally, software packages for the methods developed will be made available publicly. The investigators are committed to enhancing the pedagogical component of the proposal through advising students and developing graduate and undergraduate topic courses at their respective institutions.Motivated by the increasing need to mitigate scalability issues in Bayesian computation, variational inference has tremendously grown in popularity over the last two decades as an approximate Bayesian computational technique. Despite the proven empirical successes of variational inference in large complex data domains, systematic investigations into its statistical properties have commenced only recently. Through this project, the investigators will pose a number of foundational questions to address theoretical challenges in understanding and explaining the great empirical success of variational approximations in parameter estimation, statistical inference, and model selection, coupled with applications in novel domains. The investigators will also develop general purpose sufficient conditions to certify convergence of popularly used variational algorithms. The theoretical development will employ tools from dynamical systems, functional optimization, and optimal transport, leading to a unified treatment of statistical and algorithmic aspects of variational inference. In light of this new theory, the investigators will propose modifications to existing algorithms with certifiably better convergence behaviors.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.