Neural embedding-based machine learning models have shown promise for predicting novel links in biomedical knowledge graphs. Unfortunately, their practical utility is diminished by their lack of interpretability. Recently, the fully interpretable, rule-based algorithm AnyBURL yielded highly competitive results on many general-purpose link prediction benchmarks. However, its applicability to large-scale prediction tasks on complex biomedical knowledge bases is limited by long inference times and difficulties with aggregating predictions made by multiple rules. We improve upon AnyBURL by introducing the SAFRAN rule application framework which aggregates rules through a scalable clustering algorithm. SAFRAN yields new state-of-the-art results for fully interpretable link prediction on the established general-purpose benchmark FB15K-237 and the large-scale biomedical benchmark OpenBioLink. Furthermore, it exceeds the results of multiple established embedding-based algorithms on FB15K-237 and narrows the gap between rule-based and embedding-based algorithms on OpenBioLink. We also show that SAFRAN increases inference speeds by up to two orders of magnitude.
Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource constraints. Due to the large-scale and dynamic nature of resource coordination in hospitals and factories, human domain experts manually plan and adjust schedules on the fly. To perform this job, domain experts leverage heterogeneous strategies and rules-of-thumb honed over years of apprenticeship. What is critically needed is the ability to extract this domain knowledge in a heterogeneous and interpretable apprenticesh…
Carnegie Mellon University Silicon Valley（卡内基梅隆大学硅谷校区）；Stanford University（斯坦福大学）；Johns Hopkins University Applied Physics Laboratory（约翰·霍普金斯大学应用物理实验室）
Understanding heterogeneous multivariate time series data is important in many applications ranging from smart homes to aviation. Learning models of heterogeneous multivariate time series that are also human-interpretable is challenging and not adequately addressed by the existing literature. We propose grammar-based decision trees (GBDTs) and an algorithm for learning them. GBDTs extend decision trees with a grammar framework. Logical expressions derived from a context-free grammar are used for branching in place of simple thresholds on attributes. The added expressivity enables support for a…
Tsinghua University（清华大学）；East China Normal University（华东师范大学）
Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretabl…
Interpretable Deep Reinforcement Learning for Optimizing Heterogeneous Energy Storage Systems
East China University of Science and Technology（华东理工大学）；Nantong University（南通大学）；University of New South Wales（新南威尔士大学）；Nanyang Technological University（南洋理工大学）
Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts
National Engineering Research Center for Big Data Technology；System Lab, Cluster；Grid Computing Lab, School of Computer Science；Technology, Huazhong University of Science；DIRO, Université de Montréal \& Mila \& Canada CIFAR AI Chair, Canada
Generalized Additive Models (GAMs) have quickly become the leading choice for inherently-interpretable machine learning. However, unlike uninterpretable methods such as DNNs, they lack expressive power and easy scalability, and are hence not a feasible alternative for real-world tasks. We present a new class of GAMs that use tensor rank decompositions of polynomials to learn powerful, {\em inherently-interpretable} models. Our approach, titled Scalable Polynomial Additive Models (SPAM) is effortlessly scalable and models {\em all} higher-order feature interactions without a combinatorial param…
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions
Mederrata（梅德拉塔）；National Institutes of Health（美国国立卫生研究院）；University of Vermont（佛蒙特大学）
An Interpretable and Scalable Framework for Evaluating Large Language Models
Department of Statistics, University of California, Riverside（加州大学河滨分校统计学系）；School of Statistics and Data Science, Southeast University（东南大学统计与数据科学学院）；Department of Statistics and Data Science, Southern University of Science and Technology（南方科技大学统计与数据科学系）
Multi-task learning (MTL) aims at improving the generalization performance of several related tasks by leveraging useful information contained in them. However, in industrial scenarios, interpretability is always demanded, and the data of different tasks may be in heterogeneous domains, making the existing methods unsuitable or unsatisfactory. In this paper, following the philosophy of boosted tree, we proposed a two-stage method. In stage one, a common model is built to learn the commonalities using the common features of all instances. Different from the training of conventional boosted tree…
Graph Convolutional Network (GCN) has achieved extraordinary success in learning effective task-specific representations of nodes in graphs. However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore all possible meta-paths and extract the most useful ones for a target object, which hinders both effectiveness and interpretability; (2) they often need to generate intermediate meta-path based dense graphs, which leads to high computational complexity. To address the above issues, we propose an…
Studies involving both randomized experiments as well as observational data typically involve time-to-event outcomes such as time-to-failure, death or onset of an adverse condition. Such outcomes are typically subject to censoring due to loss of follow-up and established statistical practice involves comparing treatment efficacy in terms of hazard ratios between the treated and control groups. In this paper we propose a statistical approach to recovering sparse phenogroups (or subtypes) that demonstrate differential treatment effects as compared to the study population. Our approach involves m…
Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）
We propose $\mathcal{T}$ruth $\mathcal{T}$able net ($\mathcal{TT}$net), a novel Convolutional Neural Network (CNN) architecture that addresses, by design, the open challenges of interpretability, formal verification, and logic gate conversion. $\mathcal{TT}$net is built using CNNs' filters that are equivalent to tractable truth tables and that we call Learning Truth Table (LTT) blocks. The dual form of LTT blocks allows the truth tables to be easily trained with gradient descent and makes these CNNs easy to interpret, verify and infer. Specifically, $\mathcal{TT}$net is a deep CNN model that c…
SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
Meta；Center of Excellence for Molecular Prediction of IBD (PREDICT)（分子预测IBD中心（PREDICT））；Department of Clinical Medicine, Aalborg University（临床医学系，奥胡斯大学）；Department of Gastroenterology & Hepatology, Aalborg University Hospital（消化内科与肝病科，奥胡斯大学医院）；University of Copenhagen（哥本哈根大学）；Imperial College London（伦敦帝国理工学院）；Department of Biomedical Engineering, Tel-Aviv University（生物医学工程系，特拉维夫大学）；Sagol School of Neuroscience, Tel-Aviv University（神经科学学院，特拉维夫大学）
In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate time series and multivariate spatial time series data. Using this novel method, the proposed model can both take in a large number of heterogeneous features, such as census data, intra-county mobility, inter-county mobility, social distancing data, past growth of infection, among others, and learn complex interactions between these features. Using data collected from various sources, …
University of Michigan（密歇根大学）；Duke University（杜克大学）
Analysis of heterogeneous patterns in complex spatio-temporal data finds usage across various domains in applied science and engineering, including training autonomous vehicles to navigate in complex traffic scenarios. Motivated by applications arising in the transportation domain, in this paper we develop a model for learning heterogeneous and dynamic patterns of velocity field data. We draw from basic nonparameric Bayesian modeling elements such as hierarchical Dirichlet process and infinite hidden Markov model, while the smoothness of each homogeneous velocity field element is captured with…
This thesis focuses on data that has complex spatio-temporal structure and on probabilistic graphical models that learn the structure in an interpretable and scalable manner. We target two research areas of interest: Gaussian graphical models for tensor-variate data and summarization of complex time-varying texts using topic models. This work advances the state-of-the-art in several directions. First, it introduces a new class of tensor-variate Gaussian graphical models via the Sylvester tensor equation. Second, it develops an optimization technique based on a fast-converging proximal alternat…
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
Australian Artificial Intelligence Institute, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；Southern University of Science and Technology（南方科技大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Tokyo（东京大学）；University of New South Wales（新南威尔士大学）；Tencent WXG（腾讯微信事业群）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）
We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. Conventional dimension reduction methods aim to maximally preserve the global and/or local geometric structure of a dataset. However, in practice one is often more interested in determining how one or multiple user-selected response function(s) can be explained by the data. To intuitively connect the responses to the data, FPP constructs 2D linear embeddings optimized to reveal interpretable yet potentially non-linear patterns of the res…
New York University（纽约大学）；Facebook AI Research（Facebook人工智能研究院）
Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature smoothing, and have shown competitive results on benchmark tasks by simply operating on graph-smoothed node features, rather than using end-to-end learned feature hierarchies that are challenging to scale to large graphs. In this work, we ask whether these results can be extended to heterogeneous graphs, which encode multiple types of relationship between different entities. We propose Neighbor Averaging over Relation…
Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
Computational Sciences and Engineering Division, Oak Ridge National Laboratory（橡树岭国家实验室计算科学与工程部）；Mathematics and Computer Science Division, Argonne National Laboratory（阿贡国家实验室数学与计算机科学部）；UT-Battelle, LLC（UT-巴特勒公司）
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；School of Medicine and Health, TUM University Hospital（医学与健康学院，塔尔博恩大学医院）；School of Computation, Information and Technology, TUM（计算、信息与技术学院，塔尔博恩大学）；Weill Cornell Medicine, Cornell University, New York City（韦尔·科恩医学中心，康奈尔大学，纽约市）
Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework
Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院阜外医院、北京协和医学院）；The Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Institute of Statistics and Big Data, Renmin University of China（中国人民大学统计与大数据研究院）；National Center for Cardiovascular Diseases（国家心血管病中心）；Function Test Center, Fuwai Hospital（阜外医院功能检测中心）
PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies
Sorbonne University（索邦大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Inria（法国国家信息与自动化研究所）；Paris Brain Institute - ICM（巴黎大脑研究所 - ICM）；PSL Research university（巴黎文理研究大学）；EPHE（高等研究实践学院）
An efficient, reliable, and interpretable global solution method, the Deep learning-based algorithm for Heterogeneous Agent Models (DeepHAM), is proposed for solving high dimensional heterogeneous agent models with aggregate shocks. The state distribution is approximately represented by a set of optimal generalized moments. Deep neural networks are used to approximate the value and policy functions, and the objective is optimized over directly simulated paths. In addition to being an accurate global solver, this method has three additional features. First, it is computationally efficient in so…
xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
Computer Science and Engineering（计算机科学与工程）；INRIA Saclay；Halıcıoğlu Data Science Institute（Halıcıoğlu数据科学研究所）；UC San Diego（加州大学圣地亚哥分校）；MIT Mathematics（麻省理工学院数学系）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学博德研究所）
Physics-Guided Counterfactual Explanations for Large-Scale Multivariate Time Series: Application in Scalable and Interpretable SEP Event Prediction
Dept. of Computer Science Georgia State University Atlanta, GA, USA（计算机科学系 佐治亚州立大学）；Dept. of Computer Science California State University Fullerton, CA, USA（计算机科学系 加州州立大学Fullerton分校）
Algorithmic harmonization - the automated harmonization of a musical piece given its melodic line - is a challenging problem that has garnered much interest from both music theorists and computer scientists. One genre of particular interest is the four-part Baroque chorales of J.S. Bach. Methods for algorithmic chorale harmonization typically adopt a black-box, "data-driven" approach: they do not explicitly integrate principles from music theory but rely on a complex learning model trained with a large amount of chorale data. We propose instead a new harmonization model, called BacHMMachine, w…
