We study dynamic pricing of a product with an unknown demand distribution over a finite horizon. Departing from the standard no-regret learning environment in which prices can be adjusted at any time, we restrict price changes to predetermined points in time to reflect common retail practice. This constraint, coupled with demand model ambiguity and an unknown customer arrival pattern, imposes a high risk of revenue loss, as a price based on a misestimated demand model may be applied to many customers before it can be revised. We develop an adaptive risk learning (ARL) framework that embeds a data-driven ambiguity set (DAS) to quantify demand model ambiguity by adapting to the unknown arrival pattern. Initially, when arrivals are few, the DAS includes a broad set of plausible demand models, reflecting high ambiguity and revenue risk. As new data is collected through pricing, the DAS progressively shrinks, capturing the reduction in model ambiguity and associated risk. We establish the probabilistic convergence of the DAS to the true demand model and derive a regret bound for the ARL policy that explicitly links revenue loss to the data required for the DAS to identify the true model with high probability. The dependence of our regret bound on the arrival pattern is unique to our constrained dynamic pricing problem and contrasts with no-regret learning environments, where regret is constant and arrival-pattern independent. Relaxing the constraint on infrequent price changes, we show that ARL attains the known constant regret bound. Numerical experiments further demonstrate that ARL outperforms benchmarks that prioritize either regret or risk alone by adaptively balancing both without knowledge of the arrival pattern. This adaptive risk adjustment is crucial for achieving high revenues and low downside risk when prices are sticky and both demand and arrival patterns are unknown.
Graph embedding technics are studied with interest on public datasets, such as BlogCatalog, with the common practice of maximizing scoring on graph reconstruction, link prediction metrics etc. However, in the financial sector the important metrics are often more business related, for example fraud detection rates. With our privileged position of having large amount of real-world non-public P2P-lending social data, we aim to study empirically whether recent advances in graph embedding technics provide a useful signal for metrics more closely related to business interests, such as fraud detectio…
University of Kansas（堪萨斯大学）；University of Iowa（爱荷华大学）
Cardiovascular disease (CVD) is a serious illness affecting millions world-wide and is the leading cause of death in the US. Recent years, however, have seen tremendous growth in the area of personalized medicine, a field of medicine that places the patient at the center of the medical decision-making and treatment process. Many CVD-focused personalized medicine innovations focus on genetic biomarkers, which provide person-specific CVD insights at the genetic level, but do not focus on the practical steps a patient could take to mitigate their risk of CVD development. In this work we propose l…
Identifying and mitigating safety risks is paramount in a number of industries. In addition to guidelines and best practices, many industries already have safety management systems (SMSs) designed to monitor and reinforce good safety behaviors. The analytic capabilities to analyze the data acquired through such systems, however, are still lacking in terms of their ability to robustly quantify risks posed by various occupational hazards. Moreover, best practices and modern SMSs are unable to account for dynamically evolving environments/behavioral characteristics commonly found in many industri…
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory
University of California, Santa Barbara（加州大学圣巴巴拉分校）；Jefferson National Laboratory（杰斐逊国家实验室）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；Oak Ridge National Laboratory（橡树岭国家实验室）
RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactions
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；OSF HealthCare（OSF医疗集团）；Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校）
A Mobile Data-Driven Hierarchical Deep Reinforcement Learning Approach for Real-time Demand-Responsive Railway Rescheduling and Station Overcrowding Mitigation
Chang'an University（长安大学）；University of Leeds（利兹大学）
Laboratoire de Physique de l’Ecole Normale Supérieure（巴黎高等师范学院物理实验室）；Université PSL（巴黎文理研究大学）；CNRS（法国国家科学研究中心）；Sorbonne Université（索邦大学）；Université Paris-Diderot（巴黎第七大学）；Sorbonne Paris Cité（索邦巴黎西岱大学）；Politecnico di Torino（都灵理工大学）；Italian Institute for Genomic Medicine（意大利基因组医学研究所）；Collegio Carlo Alberto（卡洛·阿尔贝托学院）；INFN Sezione di Torino（意大利国家核物理研究所都灵分部）；The Abdus Salam International Centre for Theoretical Physics（阿卜杜斯·萨拉姆国际理论物理中心）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Contact-tracing is an essential tool in order to mitigate the impact of pandemic such as the COVID-19. In order to achieve efficient and scalable contact-tracing in real time, digital devices can play an important role. While a lot of attention has been paid to analyzing the privacy and ethical risks of the associated mobile applications, so far much less research has been devoted to optimizing their performance and assessing their impact on the mitigation of the epidemic. We develop Bayesian inference methods to estimate the risk that an individual is infected. This inference is based on the …
Risk Management for Mitigating Benchmark Failure Modes: BenchRisk
AI Verification and Evaluation Research Institute（人工智能验证与评估研究所）；Responsible AI Collaborative（负责任人工智能协作组织）；Polytechnique Montreal（蒙特利尔理工学院）；Prediction Guard（预测卫士公司）；Google（谷歌）；Google Deepmind（谷歌DeepMind）；Stanford University（斯坦福大学）；Veraitech（维莱科技公司）；University of Houston（休斯顿大学）
Modeling the risk of extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Although the available low-resolution climate models capture different scenarios, accurate risk assessment for mitigation and adaption often demands detail that they typically cannot resolve. Here, we develop a dynamic data-driven downscaling (super-resolution) method that incorporates physics and statistics in a generative framework to learn the fine-scale spatial details of rainfall. Our method transforms coarse-resolution ($0.25^{\circ} \times 0.25^{\…
We consider the setting in which an electric power utility seeks to curtail its peak electricity demand by offering a fixed group of customers a uniform price for reductions in consumption relative to their predetermined baselines. The underlying demand curve, which describes the aggregate reduction in consumption in response to the offered price, is assumed to be affine and subject to unobservable random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility mi…
Real-Time Cascade Mitigation in Power Systems Using Influence Graph Improved by Reinforcement Learning
School of Mechanical and Electrical Engineering, Soochow University（苏州大学机械与电子工程学院）；Department of Computer Science, Iowa State University（计算机科学系，爱荷华州立大学）；Department of Mechanical Engineering, University of Akron（机械工程系，阿克伦大学）；Department of Computer and Information Technology, Purdue University（计算机与信息科技系，普渡大学）
