Sloan School of Management（斯隆管理学院）；Massachusetts Institute of Technology（麻省理工学院）；Operations Research Center（运筹学研究中心）；Dep. of Electrical Engineering and Computer Science（电气工程与计算机科学系）
Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet, Sebastien Martin
When quantitative models are used to support decision-making on complex and important topics, understanding a model's ``reasoning'' can increase trust in its predictions, expose hidden biases, or reduce vulnerability to adversarial attacks. However, the concept of interpretability remains loosely defined and application-specific. In this paper, we introduce a mathematical framework in which machine learning models are constructed in a sequence of interpretable steps. We show that for a variety of models, a natural choice of interpretable steps recovers standard interpretability proxies (e.g., sparsity in linear models). We then generalize these proxies to yield a parametrized family of consistent measures of model interpretability. This formal definition allows us to quantify the ``price'' of interpretability, i.e., the tradeoff with predictive accuracy. We demonstrate practical algorithms to apply our framework on real and synthetic datasets.
Sloan School of Management（斯隆管理学院）；Massachusetts Institute of Technology（麻省理工学院）；Operations Research Center（运筹学研究中心）；Dep. of Electrical Engineering and Computer Science（电气工程与计算机科学系）
When predictive models are used to support complex and important decisions, the ability to explain a model's reasoning can increase trust, expose hidden biases, and reduce vulnerability to adversarial attacks. However, attempts at interpreting models are often ad hoc and application-specific, and the concept of interpretability itself is not well-defined. We propose a general optimization framework to create explanations for linear models. Our methodology decomposes a linear model into a sequence of models of increasing complexity using coordinate updates on the coefficients. Computing this de…
We often desire our models to be interpretable as well as accurate. Prior work on optimizing models for interpretability has relied on easy-to-quantify proxies for interpretability, such as sparsity or the number of operations required. In this work, we optimize for interpretability by directly including humans in the optimization loop. We develop an algorithm that minimizes the number of user studies to find models that are both predictive and interpretable and demonstrate our approach on several data sets. Our human subjects results show trends towards different proxy notions of interpretabi…
Exploring the Interpretability of Forecasting Models for Energy Balancing Market
Department of Informatics, University of Oslo（信息学院，奥斯陆大学）；Department of Electric Energy, Norwegian University of Science and Technology（电力能源系，挪威科学与技术大学）
Centrum Wiskunde & Informatica（荷兰国家数学与计算机科学研究所）；University of Trieste（的里雅斯特大学）；IMT School for Advanced Studies Lucca（卢卡IMT高等研究院）
Many risk-sensitive applications require Machine Learning (ML) models to be interpretable. Attempts to obtain interpretable models typically rely on tuning, by trial-and-error, hyper-parameters of model complexity that are only loosely related to interpretability. We show that it is instead possible to take a meta-learning approach: an ML model of non-trivial Proxies of Human Interpretability (PHIs) can be learned from human feedback, then this model can be incorporated within an ML training process to directly optimize for interpretability. We show this for evolutionary symbolic regression. W…
Price Interpretability of Prediction Markets: A Convergence Analysis
Industrial Bank., Co., Ltd.（兴业银行股份有限公司）；Shanghai University of Finance and Economics（上海财经大学）；Fuzhou University（福州大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Lumiata Data Science and Engineering（Lumiata数据科学与工程部）
Health insurance companies cover half of the United States population through commercial employer-sponsored health plans and pay 1.2 trillion US dollars every year to cover medical expenses for their members. The actuary and underwriter roles at a health insurance company serve to assess which risks to take on and how to price those risks to ensure profitability of the organization. While Bayesian hierarchical models are the current standard in the industry to estimate risk, interest in machine learning as a way to improve upon these existing methods is increasing. Lumiata, a healthcare analyt…
Machine learning is an increasingly popular tool with some success in predicting stock prices. One promising method is the Trader-Company~(TC) method, which takes into account the dynamism of the stock market and has both high predictive power and interpretability. Machine learning-based stock prediction methods including the TC method have been concentrating on point prediction. However, point prediction in the absence of uncertainty estimates lacks credibility quantification and raises concerns about safety. The challenge in this paper is to make an investment strategy that combines high pre…
University of Virginia（弗吉尼亚大学）；IBM Research（IBM研究院）
Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product. It is desirable for this pricing policy to be simple and interpretable, so it can be verified, checked for fairness, and easily implemented. However, efforts to incorporate machine learning into a pricing framework often lead to complex pricing policies which are not interpretable, resulting in slow adoption in practice. We present a customized, prescriptive tree-based algorithm that distills knowledge from a co…
Towards Accurate and Interpretable Time-series Forecasting: A Polynomial Learning Approach
Center for Intelligent Decision-Making（智能决策中心）；Machine Learning, School of Management, Xi'an Jiaotong University, Xi'an, China（机器学习，管理学院，西安交通大学，中国）；The 39th Research Institute of China Electronics Technology Group Corporation, Xi'an, China（中国电子科技集团第三十九研究所，中国）；Shenzhen Research Institute of Big Data, ShenZhen, China（深圳大数据研究院，中国）
TFT-ACB-XML: Decision-Level Integration of Customized Temporal Fusion Transformer and Attention-BiLSTM with XGBoost Meta-Learner for BTC Price Forecasting
University of Engineering and Applied Sciences (UEAS)（工程与应用科学大学（UEAS））；King Fahad University of Petroleum and Minerals (KFUPM)（法赫德国王石油与矿产大学（KFUPM））
