A point pattern is a dataset of coordinates, typically in 2D or 3D space. Point patterns are ubiquitous in diverse applications including Geographic Information Systems, Astronomy, Ecology, Biology and Medicine. Among the statistics used to quantify point patterns, most are based on Ripley's K-function, which measures the deviation of the observed pattern from a completely random arrangement of points. This approach is useful for constructing null hypothesis tests, but Ripley's K and its variants are less suitable as quantitative effect sizes because their ranges and expected values generally depend on the scale or the size of the region in which the pattern is observed. To address this, we propose a new function that behaves like a correlation coefficient for point patterns: it is tightly bounded by -1 and 1, with a value of -1 corresponding to a maximally dispersed arrangement of points, 0 indicating complete spatial randomness, and 1 representing maximal clustering. These properties are independent of scale and observation window size assuming appropriate edge correction. Evaluating our function on simulated data, we show that it has comparable statistical calibration and power to K-based baselines. We hope that the ease of interpretation of our bounded function will facilitate the analysis of spatial data across multiple fields.
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Data collected in the real world often encapsulates historical discrimination against disadvantaged groups and individuals. Existing fair machine learning (FairML) research has predominantly focused on mitigating discriminative bias in the model prediction, with far less effort dedicated towards exploring how to trace biases present in the data, despite its importance for the transparency and interpretability of FairML. To fill this gap, we investigate a novel research problem: discovering samples that reflect biases/prejudices from the training data. Grounding on the existing fairness notions, we lay out a sample bias criterion and propose practical algorithms for measuring and countering sample bias. The derived bias score provides intuitive sample-level attribution and explanation of historical bias in data. On this basis, we further design two FairML strategies via sample-bias-informed minimal data editing. They can mitigate both group and individual unfairness at the cost of minimal or zero predictive utility loss. Extensive experiments and analyses on multiple real-world datasets demonstrate the effectiveness of our methods in explaining and mitigating unfairness. Code is available at https://github.com/ZhiningLiu1998/AIM.
With the increasing demands of training graph neural networks (GNNs) on large-scale graphs, graph data condensation has emerged as a critical technique to relieve the storage and time costs during the training phase. It aims to condense the original large-scale graph to a much smaller synthetic graph while preserving the essential information necessary for efficiently training a downstream GNN. However, existing methods concentrate either on optimizing node features exclusively or endeavor to independently learn node features and the graph structure generator. They could not explicitly leverage the information of the original graph structure and failed to construct an interpretable graph structure for the synthetic dataset. To address these issues, we introduce a novel framework named Graph Data Condensation via Self-expressive Graph Structure Reconstruction (GCSR). Our method stands out by (1) explicitly incorporating the original graph structure into the condensing process and (2) capturing the nuanced interdependencies between the condensed nodes by reconstructing an interpretable self-expressive graph structure. Extensive experiments and comprehensive analysis validate the efficacy of the proposed method across diverse GNN models and datasets. Our code is available at https://github.com/zclzcl0223/GCSR.
We present FAST, an optimization framework for fast additive segmentation. FAST segments piecewise constant shape functions for each feature in a dataset to produce transparent additive models. The framework leverages a novel optimization procedure to fit these models ~2 orders of magnitude faster than existing state-of-the-art methods, such as explainable boosting machines[20]. We also develop new feature selection algorithms in the FAST framework to fit parsimonious models that perform well. Through experiments and case studies, we show that FAST improves the computational efficiency and interpretability of additive models.
Higher-order graph clustering aims to partition the graph using frequently occurring subgraphs (i.e., motifs), instead of the lower-order edges, as the atomic clustering unit, which has been recognized as the state-of-the-art solution in ground truth community detection and knowledge discovery. Motif conductance is one of the most promising higher-order graph clustering models due to its strong interpretability. However, existing motif conductance based graph clustering algorithms are mainly limited by a seminal two-stage reweighting computing framework, needing to enumerate all motif instances to obtain an edge-weighted graph for partitioning. However, such a framework has two-fold vital defects: (1) It can only provide a quadratic bound for the motif with three vertices, and whether there is provable clustering quality for other motifs is still an open question. (2) The enumeration procedure of motif instances incurs prohibitively high costs against large motifs or large dense graphs due to combinatorial explosions. Besides, expensive spectral clustering or local graph diffusion on the edge-weighted graph also makes existing methods unable to handle massive graphs with millions of nodes. To overcome these dilemmas, we propose a Provable and Scalable Motif Conductance algorithm PSMC, which has a fixed and motif-independent approximation ratio for any motif. Specifically, PSMC first defines a new vertex metric Motif Resident based on the given motif, which can be computed locally. Then, it iteratively deletes the vertex with the smallest motif resident value very efficiently using novel dynamic update technologies. Finally, it outputs the locally optimal result during the above iterative process. To further boost efficiency, we propose several effective bounds to estimate the motif resident value of each vertex, which can greatly reduce computational costs. Empirical results on real-life and synthetic demonstrate that our proposed algorithms achieve 3.2-32 times speedup and improve the quality by at least 12 times than the state-of-the art baselines.
Inductive relation reasoning in knowledge graphs aims at predicting missing triplets involving unseen entities and/or unseen relations. While subgraph-based methods that reason about the local structure surrounding a candidate triplet have shown promise, they often fall short in accurately modeling the causal dependence between a triplet's subgraph and its ground-truth label. This limitation typically results in a susceptibility to spurious correlations caused by confounders, adversely affecting generalization capabilities. Herein, we introduce a novel front-door adjustment-based approach designed to learn the causal relationship between subgraphs and their ground-truth labels, specifically for inductive relation prediction. We conceptualize the semantic information of subgraphs as a mediator and employ a graph data augmentation mechanism to create augmented subgraphs. Furthermore, we integrate a fusion module and a decoder within the front-door adjustment framework, enabling the estimation of the mediator's combination with augmented subgraphs. We also introduce the reparameterization trick in the fusion model to enhance model robustness. Extensive experiments on widely recognized benchmark datasets demonstrate the proposed method's superiority in inductive relation prediction, particularly for tasks involving unseen entities and unseen relations. Additionally, the subgraphs reconstructed by our decoder offer valuable insights into the model's decision-making process, enhancing transparency and interpretability.
In multivariate time series (MTS) classification, finding the important features (e.g., sensors) for model performance is crucial yet challenging due to the complex, high-dimensional nature of MTS data, intricate temporal dynamics, and the necessity for domain-specific interpretations. Current explanation methods for MTS mostly focus on time-centric explanations, apt for pinpointing important time periods but less effective in identifying key features. This limitation underscores the pressing need for a feature-centric approach, a vital yet often overlooked perspective that complements time-centric analysis. To bridge this gap, our study introduces a novel feature-centric explanation and evaluation framework for MTS, named CAFO (Channel Attention and Feature Orthgonalization). CAFO employs a convolution-based approach with channel attention mechanisms, incorporating a depth-wise separable channel attention module (DepCA) and a QR decomposition-based loss for promoting feature-wise orthogonality. We demonstrate that this orthogonalization enhances the separability of attention distributions, thereby refining and stabilizing the ranking of feature importance. This improvement in feature-wise ranking enhances our understanding of feature explainability in MTS. Furthermore, we develop metrics to evaluate global and class-specific feature importance. Our framework's efficacy is validated through extensive empirical analyses on two major public benchmarks and real-world datasets, both synthetic and self-collected, specifically designed to highlight class-wise discriminative features. The results confirm CAFO's robustness and informative capacity in assessing feature importance in MTS classification tasks. This study not only advances the understanding of feature-centric explanations in MTS but also sets a foundation for future explorations in feature-centric explanations. The codes are available at https://github.com/eai-lab/CAFO.
Univariate decision trees, commonly used since the 1950s, predict by asking questions about a single feature in each decision node. While they are interpretable, they often lack competitive predictive accuracy due to their inability to model feature correlations. Multivariate (oblique) trees use multiple features in each node, capturing high-dimensional correlations better, but sometimes they can be difficult to interpret. We advocate for a model that strikes a useful middle ground: bivariate decision trees, which use two features in each node. This typically produces trees that not only are more accurate than univariate trees, but much smaller, which offsets the small increase in node complexity and keeps them interpretable. They also help data mining by constructing new features that are useful for discrimination, and by providing a form of supervised, hierarchical 2D visualization that reveals patterns such as clusters or linear structure. We give two new algorithms to learn bivariate trees: a fast one based on CART; and a slower one based on alternating optimization with a feature regularization term, which produces the best trees while still scaling to large datasets.
Automated machine learning (AutoML) streamlines the creation of ML models, but few specialized methods have approached the challenging domain of time series forecasting. Deep neural networks (DNNs) often deliver state-of-the-art predictive performance for forecasting data, however these models are also criticized for being computationally intensive black boxes. As a result, when searching for the "best" model, it is crucial to also acknowledge other aspects, such as interpretability and resource consumption. In this paper, we propose AutoXPCR - a novel method that produces DNNs for forecasting under consideration of multiple objectives in an automated and explainable fashion. Our approach leverages meta-learning to estimate any model's performance along PCR criteria, which encompass (P)redictive error, (C)omplexity, and (R)esource demand. Explainability is addressed on multiple levels, as AutoXPCR pro-vides by-product explanations of recommendations and allows to interactively control the desired PCR criteria importance and trade-offs. We demonstrate the practical feasibility AutoXPCR across 108 forecasting data sets from various domains. Notably, our method outperforms competing AutoML approaches - on average, it only requires 20% of computation costs for recommending highly efficient models with 85% of the empirical best quality.
As an emerging interpretable technique, Generalized Additive Models (GAMs) adopt neural networks to individually learn non-linear functions for each feature, which are then combined through a linear model for final predictions. Although GAMs can explain deep neural networks (DNNs) at the feature level, they require large numbers of model parameters and are prone to overfitting, making them hard to train and scale. Additionally, in real-world datasets with many features, the interpretability of feature-based explanations diminishes for humans. To tackle these issues, recent research has shifted towards concept-based interpretable methods. These approaches try to integrate concept learning as an intermediate step before making predictions, explaining the predictions in terms of human-understandable concepts. However, these methods require domain experts to extensively label concepts with relevant names and their ground-truth values. In response, we propose CAT, a novel interpretable Concept-bAsed Taylor additive model to simplify this process. CAT does not require domain experts to annotate concepts and their ground-truth values. Instead, it only requires users to simply categorize input features into broad groups, which can be easily accomplished through a quick metadata review. Specifically, CAT first embeds each group of input features into one-dimensional high-level concept representation, and then feeds the concept representations into a new white-box Taylor Neural Network (TaylorNet). The TaylorNet aims to learn the non-linear relationship between the inputs and outputs using polynomials. Evaluation results across multiple benchmarks demonstrate that CAT can outperform or compete with the baselines while reducing the need of extensive model parameters. Importantly, it can effectively explain model predictions through high-level concepts. Source code is available at github.com/vduong143/CAT-KDD-2024.
There are many applications for which we want to learn a latent scale for subjective properties, such as the excitement of a photo or the legibility of a font; however, obtaining human-labeled data is costly and time-consuming. One oft-used method for acquiring these labels, despite the cost being quadratic in the number of items, is the method of pairwise comparisons since this method minimizes the effect of biases and generally can be used effectively outside of a controlled environment. Crowdsourcing appears to be a panacea since online platforms provide affordable access to numerous people, but these participants, judges, vary in diligence and expertise. Several methods have been proposed to assign weights to judges based on their responses relative to everyone else, the goal being to reduce exposure to poor performers, hopefully upgrading the quality of the data. Our research focuses on two natural extensions to the Bradley-Terry-Luce formulation of scaling that jointly optimize for both scale value and judge weights. While both methods appear to perform at least as well as the unweighted formulation on average with well-behaved judges, we report a previously unknown flaw, revealing that the resultant judge weights should not be interpreted as reliabilities. Consequently, these values should not be leveraged for decisions about the judges, such as for active sampling or to validate the participant pool.
Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications. While significant progress has been made in this area, fully capturing and leveraging spatiotemporal heterogeneity remains a fundamental challenge. Therefore, we propose a novel Heterogeneity-Informed Meta-Parameter Learning scheme. Specifically, our approach implicitly captures spatiotemporal heterogeneity through learning spatial and temporal embeddings, which can be viewed as a clustering process. Then, a novel spatiotemporal meta-parameter learning paradigm is proposed to learn spatiotemporal-specific parameters from meta-parameter pools, which is informed by the captured heterogeneity. Based on these ideas, we develop a Heterogeneity-Informed Spatiotemporal Meta-Network (HimNet) for spatiotemporal time series forecasting. Extensive experiments on five widely-used benchmarks demonstrate our method achieves state-of-the-art performance while exhibiting superior interpretability. Our code is available at https://github.com/XDZhelheim/HimNet.
Monitoring and maintaining machine learning models are among the most critical challenges in translating recent advances in the field into real-world applications. However, current monitoring methods lack the capability of provide actionable insights answering the question of why the performance of a particular model really degraded. In this work, we propose a novel approach to explain the behavior of a black-box model under feature shifts by attributing an estimated performance change to interpretable input characteristics. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation (XPE). We analyze the underlying assumptions and demonstrate the superiority of our approach over several baselines on different data sets across various data modalities such as images, audio, and tabular data. We also indicate how the generated results can lead to valuable insights, enabling explanatory model monitoring by revealing potential root causes for model deterioration and guiding toward actionable countermeasures.
Today, as increasingly complex predictive models are developed, simple rule sets remain a crucial tool to obtain interpretable predictions and drive high-stakes decision making. However, a single rule set provides a partial representation of a learning task. An emerging paradigm in interpretable machine learning aims at exploring the Rashomon set of all models exhibiting near-optimal performance. Existing work on Rashomon-set exploration focuses on exhaustive search of the Rashomon set for particular classes of models, which can be a computationally challenging task. On the other hand, exhaustive enumeration leads to redundancy that often is not necessary, and a representative sample or an estimate of the size of the Rashomon set is sufficient for many applications. In this work, we propose, for the first time, efficient methods to explore the Rashomon set of rule-set models with or without exhaustive search. Extensive experiments demonstrate the effectiveness of the proposed methods in a variety of scenarios.
Graph Neural Networks (GNNs) have achieved great success in Knowledge Graph Completion (KGC) by modelling how entities and relations interact in recent years. However, the explanation of the predicted facts has not caught the necessary attention. Proper explanations for the results of GNN-based KGC models increase model transparency and help researchers develop more reliable models. Existing practices for explaining KGC tasks rely on instance/subgraph-based approaches, while in some scenarios, paths can provide more user-friendly and interpretable explanations. Nonetheless, the methods for generating path-based explanations for KGs have not been well-explored. To address this gap, we propose Power-Link, the first path-based KGC explainer that explores GNN-based models. We design a novel simplified graph-powering technique, which enables the generation of path-based explanations with a fully parallelisable and memory-efficient training scheme. We further introduce three new metrics for quantitative evaluation of the explanations, together with a qualitative human evaluation. Extensive experiments demonstrate that Power-Link outperforms the SOTA baselines in interpretability, efficiency, and scalability. The code is available at https://github.com/OUTHIM/power-link
Operating and maintaining hyperscale data centers involving millions of service hosts has been an extremely intricate task to tackle for top Internet companies. Incessant system failures cost operators countless hours of browsing through performance metrics to diagnose the underlying root cause to prevent the recurrence. Although many state-of-the-art (SOTA) methods have used time-series causal discovery to construct causal relationships among anomalous metrics, they only focus on homogeneous service-level performance metrics and fail to yield useful insights on heterogeneous host-level metrics. To address the challenge, this study presents FaultInsight, a highly interpretable deep causal host fault diagnosing framework that offers diagnostic insights from various perspectives to reduce human effort in troubleshooting. We evaluate FaultInsight using dozens of incidents collected from our production environment. FaultInsight provides markedly better root cause identification accuracy than SOTA baselines in our incident dataset. It also shows outstanding advantages in terms of deployability in real production systems. Our engineers are deeply impressed by FaultInsight's ability to interpret incidents from multiple perspectives, helping them quickly understand the mechanism behind the faults.
The data science ecosystem encompasses data fairness, statistical, ML and AI methods and tools, interpretable data analysis and results, and trustworthy decision-making. Rapid advancements in AI have revolutionized data utilization and enabled machines to learn from data more effectively. Statistics, as the science of learning from data while accounting for uncertainty, plays a pivotal role in addressing complex real-world problems and facilitating trustworthy decision-making. In this talk, I will discuss the challenges and opportunities involved in building an end-to-end scalable and interpretable data science ecosystem using the analysis of whole genome sequencing studies and biobanks that integrates statistics, ML/AI, and genomic and health science as an example. Biobanks collect whole genome data, electronic health records and epidemiological data. I will illustrate key points using the analysis of multi-ancestry whole genome sequencing studies and biobanks by discussing a few scalable and interpretable statistical and ML/AI methods, tools and data science resources. Specifically, first, data fairness and diversity is a critical pillar of a trustworthy data science ecosystem. About 85+% of genome wide association study samples in the last 15 years are European, resulting in disparity in genetic research. I will discuss the community effort on improving diversity in genetic studies in the last 10 years. I will present trans-ancestry polygenic risk scores (PRS) using millions of common genetic variants across the genome by leveraging large GWAS sample sizes of European and smaller sample sizes of under-represented populations for predicting disease risk using transfer learning and genetic association summary statistics. The performance of deep learning methods for PRS will also be discussed. Second, scalability in cloud platforms is critical for large scale affordable analysis for multi-ancestry biobanks and whole genome studies. I will discuss improving scalability in cloud-computing using interpretable sparsity via FastSparseGRM. To build an interpretable and powerful end-to-end ecosystem of rare variant analysis of large scale whole genome sequencing studies and biobanks, I will first introduce FAVOR, a multi-faceted variant functional annotation database and portal of all possible 9 billions of variants across the whole genome. I will discuss FAVOR-GPT, a LLM interface of the FAVOR functional annotation database to improve user experience for navigating FAVOR and performing variant functional annotation query and variant functional summary statistics calculations. I will also discuss FAVORannotator which can be used to functionally annotate any whole genome sequencing studies. I will also discuss STAAR and STAAR and STAARpipeline, the WGS rare variant analysis pipeline that boosts the power of WGS rare variant association analysis by dynamically incorporating multi-faceted variant functional annotations. Extension of incorporating single-cell data in WGS analysis will also be discussed. I will also discuss ensemble methods that improve the power of rare variant association tests. Cloud-deployment of these resources and tools in several ecosystems will be presented, such as RAP for the UK biobank, AnVIL for the NHGRI Genome Sequencing Program and All of Us, and BioData Catalyst for the NHLBI Trans-omics Precision Medine Program (TOPMed). This talk aims to ignite proactive and thought-provoking discussions, foster collaboration, and cultivate open-minded approaches to advance scientific discovery.
This research introduces XGA-Osteo, an innovative approach that leverages Explainable Artificial Intelligence (XAI) to enhance the accuracy and interpretability of knee osteoarthritis diagnosis. Recent studies have utilized AI approaches to automate the diagnosis using knee joint X-ray images. However, these studies have primarily focused on predicting the severity of osteoarthritis without providing additional information to assist doctors in their diagnoses. In addition to accurately diagnosing the severity of the condition, XGA-Osteo generates an anomaly map, produced from a reconstructed image of a healthy knee using adversarial learning. Thus, the abnormal regions in X-ray images can be highlighted, offering valuable supplementary information to medical experts during the diagnosis process.
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks
PDF ↗LangXAI is a framework that integrates Explainable Artificial Intelligence (XAI) with advanced vision models to generate textual explanations for visual recognition tasks. Despite XAI advancements, an understanding gap persists for end-users with limited domain knowledge in artificial intelligence and computer vision. LangXAI addresses this by furnishing text-based explanations for classification, object detection, and semantic segmentation model outputs to end-users. Preliminary results demonstrate LangXAI's enhanced plausibility, with high BERTScore across tasks, fostering a more transparent and reliable AI framework on vision tasks for end-users. The code and demo of this work can be found at https://analytics-everywhere-lab.github.io/langxai.io/.
PyXAI (Python eXplainable AI) is a Python library designed for providing explanations and cor- recting tree-based Machine Learning (ML) models. It is suited to decision trees, random forests, and boosted trees, when used for regression or classification tasks. In contrast to many model-agnostic approaches to XAI, PyXAI exploits the model it- self to generate explanations, ensuring them to be faithful. PyXAI includes several algorithms for the generation of explanations, which can be abductive or contrastive. PyXAI also includes algorithms for correcting tree-based models when their predictions conflict with pieces of user knowledge.