With the rise of the digital economy and an explosion of available information about consumers, effective personalization of goods and services has become a core business focus for companies to improve revenues and maintain a competitive edge. This paper studies the personalization problem through the lens of policy learning, where the goal is to learn a decision-making rule (a policy) that maps from consumer and product characteristics (features) to recommendations (actions) in order to optimize outcomes (rewards). We focus on using available historical data for offline learning with unknown data collection procedures, where a key challenge is the non-random assignment of recommendations. Moreover, in many business and medical applications, interpretability of a policy is essential. We study the class of policies with linear decision boundaries to ensure interpretability, and propose learning algorithms using tools from causal inference to address unbalanced treatments. We study several optimization schemes to solve the associated non-convex, non-smooth optimization problem, and find that a Bayesian optimization algorithm is effective. We test our algorithm with extensive simulation studies and apply it to an anonymized online marketplace customer purchase dataset, where the learned policy outputs a personalized discount recommendation based on customer and product features in order to maximize gross merchandise value (GMV) for sellers. Our learned policy improves upon the platform's baseline by 88.2\% in net sales revenue, while also providing informative insights on which features are important for the decision-making process. Our findings suggest that our proposed policy learning framework using tools from causal inference and Bayesian optimization provides a promising practical approach to interpretable personalization across a wide range of applications.
We study the problem of learning to choose from m discrete treatment options (e.g., news item or medical drug) the one with best causal effect for a particular instance (e.g., user or patient) where the training data consists of passive observations of covariates, treatment, and the outcome of the treatment. The standard approach to this problem is regress and compare: split the training data by treatment, fit a regression model in each split, and, for a new instance, predict all m outcomes and pick the best. By reformulating the problem as a single learning task rather than m separate ones, w…
Pittsburgh, PA 15213, USA（美国宾夕法尼亚州匹兹堡（注：该地址不属于机构，可能是原数据错误，按要求应忽略，此处仅为示例说明））
Recent work has found that adversarially-robust deep networks used for image classification are more interpretable: their feature attributions tend to be sharper, and are more concentrated on the objects associated with the image's ground-truth class. We show that smooth decision boundaries play an important role in this enhanced interpretability, as the model's input gradients around data points will more closely align with boundaries' normal vectors when they are smooth. Thus, because robust models have smoother boundaries, the results of gradient-based attribution methods, like Integrated G…
Towards interpretable AI with quantum annealing feature selection
BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain（BCN医疗科技，庞培法布拉大学，巴塞罗那，西班牙）；Barcelona Supercomputing Center (BSC)（巴塞罗那超级计算中心（BSC））；Departament de Física Quàntica i Astrofísica, Facultat de Física, Universitat de Barcelona（巴塞罗那大学物理量子与天体物理系，物理系）；Institut de Ciències del Cosmos, Universitat de Barcelona, ICCUB（巴塞罗那大学宇宙科学研究所，ICCUB）；ICREA, Barcelona, Spain（ICREA，巴塞罗那，西班牙）
QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum-Classical Neural Network
Khulna University of Engineering and Technology (KUET)（库尔纳工程技术大学（KUET））；American International University-Bangladesh (AIUB)（孟加拉国美国国际大学（AIUB））；The University of Aizu（会津大学）
Bernoulli Institute, University of Groningen（格罗宁根大学伯努利研究所）；School of Computer Science, University of Birmingham（伯明翰大学计算机学院）
Increasing number of sectors which affect human lives, are using Machine Learning (ML) tools. Hence the need for understanding their working mechanism and evaluating their fairness in decision-making, are becoming paramount, ushering in the era of Explainable AI (XAI). In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem. These models are also capable of visualisation of the classifier and decision boundaries: they are the angle based variants…
Internet of things (IoT) applications have become increasingly popular in recent years, with applications ranging from building energy monitoring to personal health tracking and activity recognition. In order to leverage these data, automatic knowledge extraction - whereby we map from observations to interpretable states and transitions - must be done at scale. As such, we have seen many recent IoT data sets include annotations with a human expert specifying states, recorded as a set of boundaries and associated labels in a data sequence. These data can be used to build automatic labeling algo…
AdaptStress: Online Adaptive Learning for Interpretable and Personalized Stress Prediction Using Multivariate and Sparse Physiological Signals
Engineering and Technology institute Groningen (ENTEG), University of Groningen（格罗宁根大学工程与技术学院（ENTEG））；Department of Human Movement Sciences, University Medical Center Groningen（格罗宁根大学医学中心人类运动科学系）
On the one hand, artificial neural networks (ANNs) are commonly labelled as black-boxes, lacking interpretability; an issue that hinders human understanding of ANNs' behaviors. A need exists to generate a meaningful sequential logic of the ANN for interpreting a production process of a specific output. On the other hand, decision trees exhibit better interpretability and expressive power due to their representation language and the existence of efficient algorithms to transform the trees into rules. However, growing a decision tree based on the available data could produce larger than necessar…
National Research Council of Canada（加拿大国家研究委员会）；Virginia Commonwealth University（弗吉尼亚联邦大学）；Naval Research Laboratory（美国海军研究实验室）
Deep learning models are being increasingly applied to imbalanced data in high stakes fields such as medicine, autonomous driving, and intelligence analysis. Imbalanced data compounds the black-box nature of deep networks because the relationships between classes may be highly skewed and unclear. This can reduce trust by model users and hamper the progress of developers of imbalanced learning algorithms. Existing methods that investigate imbalanced data complexity are geared toward binary classification, shallow learning models and low dimensional data. In addition, current eXplainable Artific…
Pushing the (Decision) Boundaries: Dynamically Calibrating Differentially Private Noise to Explainability in Federated Learning
School of Industrial & Intelligent Systems Engineering, Tel Aviv University（特拉维夫大学工业与智能系统工程学院）；School of Integrated Technology, Yonsei University（延世大学融合技术学院）
