Towards more efficient machine learning algorithms: theory and practice

迈向更高效的机器学习算法：理论与实践

• 批准号: RGPIN-2016-05942
• 负责人: Marchand, Mario
• 金额: $ 2.77万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=687235
• 关键词: Towards; more; efficient; machine; learning

Machine learning is concerned with the development of intelligent computer systems that are able to learn and generalize from collected data. This ability is required for many important tasks that are just too complex to be explicitly programmed by humans such as recognizing voice directives on smart phones, or recognizing faces in images, or displaying the best results from a query. The best computer systems that achieve these tasks have in common the fact that their ability has been acquired by running learning algorithms on vast amounts of data. Many of these tasks are now so vital to our economy that machine learning-based technology is now commonplace. However, that technology needs to be greatly improved. Indeed, credit cards are too frequently being blocked, we are still receiving too many undesirable emails, and automatic speech recognition is still not satisfactory. Building more powerful hardware is just part of the solution as we must also find the most efficient learning algorithms. Consequently, the proposed research program aims at addressing the important problem of how to improve and modify existing learning algorithms such that they yield better predictors while using a minimal (or, at least, an acceptable) amount of resources. To meet this objective we plan to approach challenging machine-learning problems from both a theoretical and a practical perspective. Theoretical analysis is needed because we want to find efficient learning algorithms with provable guarantees. More specifically, we plan, in the short term, to improve the existing learning algorithms for structured output prediction and domain adaptation. These are currently two challenging machine-learning problems that model situations often encountered in practice but for which we do not have yet satisfactory learning algorithms. We also plan to find the most efficient boosting-type algorithms for learning from large-scale data sets. Finally, in the long term, we plan to expand the set covering machine and the decision list machine for predicting phenotypes from genomic data. These are learning algorithms (that we have proposed in the past) that produce uncharacteristically sparse predictors that are easy to interpret. Hence, these predictors can, in principle, help us to uncover the genomic cause of a phenotype. The impressive results we have obtained recently on the prediction of antibiotic resistance from bacterial genomes motivate us to consider, in the short term, the more ambitious problem of predicting cancer types from human sequences, which could be DNA, RNA, or protein sequences. It is thus expected that this research program will yield provably efficient learning methods for different machine learning-based applications that, in turn, will improve our quality of life. Moreover, it is expected that this research program will deliver the equivalent of two M.Sc graduates and four Ph.D graduates. **

机器学习涉及智能计算机系统的开发，这些系统能够从收集的数据中学习和概括。许多重要任务都需要这种能力，这些任务太复杂而无法由人类显式编程，例如识别智能手机上的语音指令，或识别图像中的面部，或显示查询的最佳结果。实现这些任务的最佳计算机系统的共同点是，它们的能力是通过在大量数据上运行学习算法来获得的。其中许多任务现在对我们的经济至关重要，以至于基于机器学习的技术现在已经司空见惯。然而，这项技术需要大大改进。事实上，信用卡被频繁封锁，我们仍然收到太多不良电子邮件，自动语音识别仍然不能令人满意。 构建更强大的硬件只是解决方案的一部分，因为我们还必须找到最有效的学习算法。因此，拟议的研究计划旨在解决如何改进和修改现有学习算法的重要问题，以便在使用最少（或至少是可接受的）资源的同时产生更好的预测器。为了实现这一目标，我们计划从理论和实践的角度来解决具有挑战性的机器学习问题。需要理论分析，因为我们希望找到具有可证明保证的有效学习算法。更具体地说，我们计划在短期内改进结构化输出预测和领域适应的现有学习算法。这是目前两个具有挑战性的机器学习问题，它们对实践中经常遇到的情况进行建模，但我们还没有令人满意的学习算法。我们还计划找到最有效的提升型算法来从大规模数据集中学习。最后，从长远来看，我们计划扩展集合覆盖机和决策列表机，用于从基因组数据预测表型。这些是学习算法（我们过去提出过），可以产生易于解释的异常稀疏的预测变量。因此，这些预测因子原则上可以帮助我们揭示表型的基因组原因。最近，我们在根据细菌基因组预测抗生素耐药性方面取得了令人印象深刻的结果，这促使我们在短期内考虑更雄心勃勃的问题，即根据人类序列（可能是 DNA、RNA 或蛋白质序列）预测癌症类型。 因此，预计该研究计划将为不同的基于机器学习的应用提供可证明有效的学习方法，从而提高我们的生活质量。 此外，预计该研究项目将培养相当于两名硕士毕业生和四名博士毕业生。 **