CIF: Small: Alpha Loss: A New Framework for Understanding and Trading Off Computation, Accuracy, and Robustness in Machine Learning

机器学习（ML）和人工智能（AI）革命的核心是使用大量数据训练的模型。鉴于越来越多地使用这种数据驱动的建模，迫切需要理解和利用各种性能特征之间的权衡，例如准确性（统计效率）、计算速度（计算效率）和鲁棒性（例如对噪声、对抗性篡改和不平衡或有偏差的数据）。该项目开发了一个统一而强大的框架，通过引入α -损失函数家族来理解和权衡这些方面——经常使用的损失函数，如0-1损失、对数损失和指数损失，作为α -损失框架的实例出现。在过去的几年里，我们看到越来越多的倡导者、监管机构和科学家认识到，数据驱动的推理和决策引擎对确保非歧视、公平和包容的代表性构成了重大挑战。阿尔法损失框架，结合几项技术进步，将允许从业者将公平性作为一个明确的旋钮，在机器学习模型的开发过程中进行调整。这项工作的广泛影响还包括为高中生开发为期一周的夏令营ML模块，以及为这些学生提供研究机会。该项目：(i)发展了损耗景观作为调谐参数alpha函数的行为的理论结果，从而阐明了行业标准对数损耗的价值和局限性，（ii）建立了精度-速度权衡和泛化界限，以及（iii）设计了实用的自适应算法，保证了超参数alpha的调整，以实现沿着权衡的各种操作点。本课题通过影响函数理论建立了α -损失的鲁棒性。通过引入噪声和对抗示例的急需模型，本工作开发了一种原则性方法，选择略大于1的alpha来设计对噪声和对手更具鲁棒性的模型。使用影响函数和约束学习设置（如公平分类），本项目研究将alpha调至1以下的有效性，以增强对高度不平衡训练数据集中有限样本的灵敏度。最后，该项目还开发了alpha-Boost作为可调增强算法，具有保证收敛性，对噪声的鲁棒性，并在需要时在线自适应。通过在合成和公开可用的真实数据集上对算法进行严格测试，在项目的每个阶段都加强了研究。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

At the heart of the machine learning (ML) and artificial intelligence (AI) revolution are models that are trained using vast amounts of data. Given the increasing use of such data-driven modeling, there is an urgent need to understand and leverage the tradeoffs between various performance characteristics such as accuracy (statistical efficiency), computational speed (computational efficiency), and robustness (say to noise, adversarial tampering, and imbalance or biased data). This project develops a unified and powerful framework for understanding and trading off these facets by introducing the family of alpha-loss functions -- often-used loss functions such as the 0-1 loss, the log-loss, and the exponential-loss appear as instantiations of the alpha-loss framework. Over the past few years, we have seen a steadily growing recognition amongst advocates, regulators, and scientists that data-driven inference and decision engines pose significant challenges for ensuring non-discrimination, and fair and inclusive representation. The alpha-loss framework, combined with several technological advances, will allow practitioners to incorporate fairness as an explicit knob to be tuned during the development of machine learning models. Broader impacts of this work also include developing ML modules for a week-long summer camp for high school students as well as providing research opportunities for such students. This project: (i) develops theoretical results on the behavior of the loss landscape as a function of the tuning parameter alpha, thereby illuminating the value and limitation of the industry standard log-loss, (ii) establishes accuracy-speed tradeoffs and generalization bounds, and (iii) designs practical adaptive algorithms with guarantees for tuning the hyperparameter alpha to achieve various operating points along the tradeoff. This project establishes the robustness properties of alpha-loss via the theory of influence functions. By introducing much-needed models for noise and adversarial examples, this work develops a principled method to choose alpha slightly larger than 1 to design models more robust to noise and adversaries. Using both influence functions and constrained learning settings such as fair classification, this project studies the efficacy of tuning alpha below one in order to enhance sensitivity to limited samples in highly imbalanced training datasets. Finally, this project also develops alpha-Boost as a tunable boosting algorithm with guaranteed convergence, robustness to noise and, where needed, online adaptation. Research is enhanced at every stage of this project through rigorous testing of algorithms on both synthetic and publicly available real datasets.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.