Optimal transport (OT) is a widely used technique for distribution alignment, with applications throughout the machine learning, graphics, and vision communities. Without any additional structural assumptions on trans-port, however, OT can be fragile to outliers or noise, especially in high dimensions. Here, we introduce a new form of structured OT that simultaneously learns low-dimensional structure in data while leveraging this structure to solve the alignment task. Compared with OT, the resulting transport plan has better structural interpretability, highlighting the connections between individual data points and local geometry, and is more robust to noise and sampling. We apply the method to synthetic as well as real datasets, where we show that our method can facilitate alignment in noisy settings and can be used to both correct and interpret domain shift.
Recently, data collaboration (DC) analysis has been developed for privacy-preserving integrated analysis across multiple institutions. DC analysis centralizes individually constructed dimensionality-reduced intermediate representations and realizes integrated analysis via collaboration representations without sharing the original data. To construct the collaboration representations, each institution generates and shares a shareable anchor dataset and centralizes its intermediate representation. Although, random anchor dataset functions well for DC analysis in general, using an anchor dataset w…
We reduce a broad class of machine learning problems, usually addressed by EM or sampling, to the problem of finding the $k$ extremal rays spanning the conical hull of a data point set. These $k$ "anchors" lead to a global solution and a more interpretable model that can even outperform EM and sampling on generalization error. To find the $k$ anchors, we propose a novel divide-and-conquer learning scheme "DCA" that distributes the problem to $\mathcal O(k\log k)$ same-type sub-problems on different low-D random hyperplanes, each can be solved by any solver. For the 2D sub-problem, we present a…
AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Informatics, Xiamen University（厦门大学信息学院）；State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University（东北大学过程工业综合自动化国家重点实验室）；Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系）
Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）；Determined AI
Despite increasing interest in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and the high-level goals of consumers' use cases. In this work, we synthesize foundational work on IML methods and evaluation into an actionable taxonomy. This taxonomy serves as a tool to conceptualize the gap between researchers and consumers, illustrated by the lack of connections between its methods and use cases components. It also provides the foundation from which we describe a three-step workflow to better enable …
New Jersey Institute of Technology（新泽西理工学院）；Wells Fargo Bank（富国银行）；University of Oregon（俄勒冈大学）
In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. Different from existing approaches, our algorithm considers contextual correlation among words, described in domain knowledge ontologies, to generate semantic explanations. To narrow down the search space for explanations, which is a major problem of long and complicated text data, we design a learnable anchor algorithm, to better extract explanations locally. A set of regulations is further introduced, regarding comb…
Improving Asset Allocation in a Fast Moving Consumer Goods B2B Company: An Interpretable Machine Learning Framework for Commercial Cooler Assignment Based on Multi-Tier Growth Targets
Fast Moving Consumer Goods B2B Company（快速消费品B2B公司）
Analyzing temporal data (e.g., wearable device data) requires a decision about how to combine information from the recent and distant past. In the context of classifying sleep status from actigraphy, Webster's rescoring rules offer one popular solution based on the long-term patterns in the output of a moving-window model. Unfortunately, the question of how to optimize rescoring rules for any given setting has remained unsolved. To address this problem and expand the possible use cases of rescoring rules, we propose rephrasing these rules in terms of epoch-specific features. Our features take …
Prototype Learning to Create Refined Interpretable Digital Phenotypes from ECGs
1Pritzker School of Medicine, University of Chicago, IL, USA；2Center for Computational Medicine \& Clinical AI, Section of Biomedical Data Science, Department of Medicine, University of Chicago, IL, USA；3Division of Cardiovascular Medicine, Department of Internal Medicine, University of California Davis, CA, USA；4Section of Hospital Medicine, Department of Medicine, University of Chicago, IL, USA
DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments
Department of Experimental Physics, European Organization for Nuclear Research (CERN)（欧洲核子研究中心（CERN）实验物理学系）；Laboratory for Nuclear Science, Massachusetts Institute of Technology (MIT)（麻省理工学院核科学实验室）
Tulane University（杜兰大学）；Central South University（中南大学）；Mind Research Network（心智研究网络）；University of Nebraska Medical Center（内布拉斯加大学医学中心）；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Multimodal fusion benefits disease diagnosis by providing a more comprehensive perspective. Developing algorithms is challenging due to data heterogeneity and the complex within- and between-modality associations. Deep-network-based data-fusion models have been developed to capture the complex associations and the performance in diagnosis has been improved accordingly. Moving beyond diagnosis prediction, evaluation of disease mechanisms is critically important for biomedical research. Deep-network-based data-fusion models, however, are difficult to interpret, bringing about difficulties for st…
