Prediction in a small-sized sample with a large number of covariates, the "small n, large p" problem, is challenging. This setting is encountered in multiple applications, such as precision medicine, where obtaining additional samples can be extremely costly or even impossible, and extensive research effort has recently been dedicated to finding principled solutions for accurate prediction. However, a valuable source of additional information, domain experts, has not yet been efficiently exploited. We formulate knowledge elicitation generally as a probabilistic inference process, where expert knowledge is sequentially queried to improve predictions. In the specific case of sparse linear regression, where we assume the expert has knowledge about the values of the regression coefficients or about the relevance of the features, we propose an algorithm and computational approximation for fast and efficient interaction, which sequentially identifies the most informative features on which to query expert knowledge. Evaluations of our method in experiments with simulated and real users show improved prediction accuracy already with a small effort from the expert.
LEAP: Likelihood Elicitation and Aggregation for LLM-based Probabilistic Forecasting
Shandong University（山东大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Zhejiang University（浙江大学）；Ningbo Global Innovation Center, Zhejiang University（浙江大学宁波科创中心）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Helsinki Institute for Information Technology HIIT（赫尔辛基信息技术研究所（HIIT））；Aalto University（阿尔托大学）；Institute for Molecular Medicine Finland (FIMM)（芬兰分子医学研究所（FIMM））；University of Helsinki（赫尔辛基大学）
Constraint-aware Learning of Probabilistic Sequential Models for Multi-Label Classification
Ukrainian Catholic University Lviv Ukraine；HUN-REN Alfr\' e d R\' e nyi Institute of Mathematics E \"o tv \"o s Lor\' a nd University Budapest Hungary；University of Oxford Oxford UK；Ukrainian Catholic University；HUN-REN Alfr\' e d R\' e nyi Institute of Mathematics；E \"o tv \"o s Lor\' a nd University；University of Oxford
Compressed Inference for Probabilistic Sequential Models
Department of Electrical Engineering（电气工程系）；Mitsubishi Electric Research Laboratories（三菱电机研发实验室）；Princeton University（普林斯顿大学）
隐马尔可夫模型（Hidden Markov Models，简称HMM）和条件随机场（Conditional Random Fields，简称CRF）是两种常用的序列数据建模技术。推理算法设计的CRF和阻碍允许估计的状态序列的意见。在一些应用中，状态序列的估计不是最终目标;相反，目标是计算它的一些函数。在这种情况下，通过传统的推理技术估计状态序列，然后从估计值计算函数映射不一定是最优的。一种更正式的方法是直接从观察结果中推断出最终结果。特别是，我们考虑的具体实例的问题，其目标是找到没有确切的过渡点的状态轨迹，并得出一种新的多项式时间推理算法，优于香草推理技术。我们发现，这个特殊的问题通常出现在许多不同的应用程序，并提出了对其中三个实验：（1）玩具机器人跟踪;（2）单笔字符识别;（3）手写字识别。
