Collaborative Online Optimization for Efficient Model-Based Learning

人工智能（AI）和机器学习（ML）的重大挑战之一是构建可以从真实的数据中学习的智能系统。为了从流数据中学习，需要在线优化和预测的新方法。目前的方法假设梯度（或损失）的顺序可用性，在实施中构成了实际障碍。我们提出了两种使用基于模型的学习来解决这一差距的方法。这些方法旨在分别利用分布式计算架构（以划分所需的计算工作）或通信网络（以有效地聚合不同的数据）。本文中介绍的协作在线优化算法和理论扩展具有广泛的应用领域，如语音识别和计算机视觉、自动驾驶汽车、交通、神经科学和商业分析。大多数经典ML算法都是在数据集已经以批处理形式可用的假设下开发的。从离线学习过渡到在线学习在许多应用领域面临着一个主要的实际障碍，在这些应用领域中，学习者不知道目标函数的封闭形式。在处理流数据时，这种黑盒属性导致延迟（由于数据或计算）与模型识别的速度和准确性之间的自然权衡。分布式计算架构提供了一种减少延迟的方法，以在必要的时间尺度内获得合理准确的模型。我们建议研究快速分布式异步随机梯度方法在线学习，其中多个工人（处理器）之间的协调异步交互精心设计。改进的准确性和速度也可以通过接收不同数据流的学习器网络来联合实现。因此，我们还考虑了分散的在线学习模型，多个学习代理通过网络进行通信。通过与网络中的其他代理共享预测或估计的能力，集体可以聚合不同的信息，以超越任何单独识别的模型（在准确性和速度方面）。最后，我们考虑的情况下，数据流有图结构。流图结构数据出现在不同的应用领域中，例如运输网络、社交网络和生物学中发现的其他网络，其中图捕获数据中的相关性。该提案包括开发一门新的研究生课程，旨在为工程专业的学生提供最先进的分布式在线优化技术方面的工作知识。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

One of the grand challenges in Artificial Intelligence (AI) and Machine Learning (ML) is building intelligent systems that can learn from data in real time. To learn from streaming data, there is need for novel approaches in online optimization and prediction. Current methods assume sequential availability of gradients (or loss), posing a practical hurdle in implementation. We propose two approaches to address this gap using model-based learning. These approaches are aimed at respectively exploiting, a distributed computing architecture (to divide the required computational effort) or a communications network (to efficiently aggregate disparate data). The collaborative online optimization algorithms and theoretic extensions introduced in this work have a broad range of applications domains such as speech recognition and computer vision, autonomous vehicles, transportation, neuroscience, and business analytics.Most of classical ML algorithms have been developed under the assumption that data sets are already available in batch form. Transitioning from offline to online learning faces a major practical hurdle in many application domains where the closed-form of the objective function is unknown to the learner. When dealing with streaming data, this black-box property leads to a natural trade-off between delays (due to data or computation) and the speed and accuracy with which a model can be identified. A distributed computing architecture provides a way to reduce delays to obtain reasonably accurate models in the necessary timescale. We propose to study fast distributed asynchronous stochastic gradient approaches for online learning in which coordination between multiple workers (processors) interacting asynchronously is carefully engineered. Improved accuracy and speed may also be jointly achieved by a network of learners receiving different streams of data. Thus, we also consider decentralized models of online learning with multiple learning agents that communicate over a network. With the ability to share predictions or estimates with other agents in a network, the collective can aggregate disparate information in a way to outperform (in terms of accuracy and speed) any individually identified model. Finally, we consider the case in which data streams have graph structure. Streaming graph structure data arises in diverse application domains such as transportation networks, social networks and other networks found in biology, where the graph captures the correlation in data. The proposal includes the development of a new graduate course aimed at providing engineering students with working knowledge on state-of-the-art distributed online optimization techniques.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.