LIMIS improves locally interpretable models by selecting and distilling key instances.
problem Low fidelity of locally interpretable models.
method LIMIS uses instance-wise subsampling guided by policy gradient and reward to improve fidelity.
result LIMIS near-matches black-box model accuracy while significantly improving fidelity.
New framework learns interpretable rule ensembles without sacrificing accuracy.
problem Trade-off between accuracy and interpretability in rule ensembles.
method Introduces local interpretability and a regularizer to promote it, using coordinate descent with local search.
result Learns rule ensembles with fewer rules to explain individual predictions, maintaining comparable accuracy.
The increasing adoption of machine learning tools has led to calls for accountability via model interpretability. But what does it mean for a machine learning model to be interpretable by humans, and how can this be assessed? We focus on two definitions of interpretability that have been introduced in the machine learn…
Proposes a model to interpret complex ML algorithms.
problem Complex ML models are hard to interpret.
method Uses model-based regression trees and interpretable main-effects models.
result Surrogate model provides interpretable results with good predictive performance.
Study evaluates local explanation methods for time series forecasting.
problem Lack of local interpretability methods for multivariate time series forecasting.
method Proposed two novel evaluation metrics: Area Over the Perturbation Curve for Regression and Ablation Percentage Threshold.
result Comprehensive comparison of local explanation models on two datasets.
LI-ITR combines flexible ML with interpretable approximations for personalized treatment rules.
problem Combining flexibility and interpretability in personalized treatment rules.
method Uses variational autoencoders and a mixture of interpretable experts.
result Accurately recovers true local coefficients and optimal treatment strategies.
A new method improves the interpretability of data-driven models in ironmaking processes.
problem Lack of transparency in machine learning models used in industrial processes.
method Combines Variational Autoencoder (VAE) with Local Interpretable Model-agnostic Explanations (LIME) for better model interpretability.
result Improved local fidelity of local interpretable linear models compared to LIME.
Model interpretability is an increasingly important component of practical machine learning. Some of the most common forms of interpretability systems are example-based, local, and global explanations. One of the main challenges in interpretability is designing explanation systems that can capture aspects of each of th…
Unified method for local GBDT feature contributions.
problem Need for local model interpretation for GBDT.
method Unified computation mechanism for instance-level feature contributions.
result Unified computation mechanism for GBDT feature contributions.
Proposes GPR with local explanation for feature interpretation.
problem Lack of feature interpretation in Gaussian process regression.
method Introduces a locally linear model to explain feature contributions in GPR predictions.
result Achieves comparable predictive performance to GPR and superior interpretability.
The problem of explaining deep learning models, and model predictions generally, has attracted intensive interest recently. Many successful approaches forgo global approximations in order to provide more faithful local interpretations of the model's behavior. LIME develops multiple interpretable models, each approximat…
CLE explains any classifier predictions in plain terms.
problem Understanding complex classifier predictions.
method Model-agnostic explanation technique approximating locally using interpretable models.
result CLE provides faithful and interpretable explanations for various classifiers.
Locally adaptive interpretable regression improves linear regression's predictability.
problem Linear regression's predictability is limited; it lacks adaptability.
method Locally adaptive interpretable regression (LoAIR) uses neural networks to predict percentile of a Gaussian distribution for regression coefficients.
result LoAIR achieves comparable or better predictive performance than state-of-the-art baselines.
This paper simplifies deep ReLU networks into local linear models for better interpretability.
problem Limited transparency and interpretability of deep neural networks, especially ReLU networks.
method Local linear representation and equivalent set of local linear models (LLMs).
result Simplified deep ReLU networks for better interpretability and diagnostics.
In this work, we present an application of Locally Interpretable Machine-Agnostic Explanations to 2-D chemical structures. Using this framework we are able to provide a structural interpretation for an existing black-box model for classifying biologically produced fuel compounds with regard to Research Octane Number. T…
We introduce a method, KL-LIME, for explaining predictions of Bayesian predictive models by projecting the information in the predictive distribution locally to a simpler, interpretable explanation model. The proposed approach combines the recent Local Interpretable Model-agnostic Explanations (LIME) method with ideas …
Machine learning and especially deep learning have garneredtremendous popularity in recent years due to their increased performanceover other methods. The availability of large amount of data has aidedin the progress of deep learning. Nevertheless, deep learning models areopaque and often seen as black boxes. Thus, the…
Improved interpretability methods for ML models using local regressions and variable importance.
problem Inability of existing interpretability methods to provide reliable explanations for ML models, especially in high-dimensional problems with irrelevant features and non-linear relationships.
method Introduces VarImp and SupClus methods using local regressions with weighted distance considering variable importance.
result VarImp and SupClus methods yield better explanations than state-of-the-art approaches, especially in high-dimensional problems with irrelevant features and non-linear relationships.
Locally sparse neural networks improve interpretability for biomedical tabular data.
problem Overfitting and lack of interpretability in neural networks for tabular biomedical data.
method Locally sparse neural network with a gating network to select relevant features.
result The method outperforms state-of-the-art models in synthetic and real-world biomedical datasets.
A new method improves LIME for better model explanation.
problem Current LIME explanations are not faithful and weak in understanding.
method Proposes a novel Modified Perturbed Sampling (MPS) for LIME.
result MPS-LIME achieves better performance in understandability, fidelity, and efficiency.
Method extracts time-localized clusters to explain deep learning models in ECG analysis.
problem Limited understanding of deep learning models in ECG analysis.
method Extracts time-localized clusters from model's internal representations.
result Enhances trust in AI-driven diagnostics and reveals clinically relevant patterns.
NGSLL combines DNN accuracy with linear model interpretability.
problem Combining high accuracy of DNNs with interpretability of linear models.
method Neural generators of sparse local linear models (NGSLL) using DNNs to approximate non-linear functions.
result Effective in real-world datasets, achieving high predictive performance and interpretability.
Local surrogate model improves time series forecasts and provides interpretable explanations.
problem Improving time series forecasting accuracy while maintaining interpretability.
method A local surrogate model is used to correct the base model's predictions, making the corrections interpretable by re-fitting the base model to the error-predicted data.
result The method can discover and explain underlying patterns in the data, improving both accuracy and interpretability.
The paper connects machine learning interpretability with learning theory.
problem Performance and explanation generalization in local machine learning models.
method Theoretical analysis and empirical validation of local approximation explanations.
result Theoretical bounds on test-time accuracy and explanation generalization.
NLS simplifies complex predictions into easy-to-understand solutions.
problem Hard-to-interpret predictions from advanced machine learning models.
method Uses a neural network with a local linear shape to output layer.
result NLS provides comparable predictive power to state-of-the-art models but is easier to interpret.
GraphLIME explains GNN models by selecting key features locally.
problem Explaining the effectiveness of GNN models is challenging due to complex nonlinear transformations.
method GraphLIME uses HSIC Lasso for nonlinear feature selection in GNN models.
result GraphLIME provides more descriptive explanations than existing methods.
As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high predictive accuracy, become important. One important requirement is transparency, which has been associated with model interpretability. Many machine learning algorithms induce models diff…
As we rely more and more on machine learning models for real-life decision-making, being able to understand and trust the predictions becomes ever more important. Local explainer models have recently been introduced to explain the predictions of complex machine learning models at the instance level. In this paper, we p…
Unified approach for interpretable regression with flexible modeling.
problem Combining predictive adaptivity with interpretability in heterogeneous data.
method Combining random Fourier features, spectral feature map, principal component analysis, Gaussian mixture model, and cluster-specific generalized additive models.
result Consistently improves upon classical and black-box models across benchmark datasets.
A method interprets black-box models using an ensemble of gradient boosting machines.
problem Local and global interpretation of black-box models.
method An ensemble of gradient boosting machines (GBMs) to form a generalized additive model.
result Efficiency and properties demonstrated on synthetic and real datasets.
The paper introduces closed-form expressions for interpreting Tsetlin Machines.
problem Interpreting complex Tsetlin Machines with a large number of clauses.
method Developed closed-form expressions for local and global interpretability of Tsetlin Machines.
result The expressions enable real-time feature importance assessment and data clustering.
Improved local feature attributions using neighbourhood reference distributions.
problem Misleading results from global population in local model behaviour.
method Formulation of neighbourhood reference distributions and self-normalised importance sampling.
result Neighbourhood Shapley values provide meaningful sparse feature attributions.
Develops transparent global models consistent with local explanations.
problem Creating globally interpretable models that align with local explanations from black-box models.
method Custom boolean features from sparse local contrastive explanations are used to train a globally transparent model.
result Custom transparent models have higher local consistency compared to other strategies.
The paper investigates interpretability techniques for deep learning models in medical data.
problem Understanding the logic behind predictions of black-box models in medical decision-making.
method Applied deep neural networks and random forests to a medical dataset. Used autoencoders and local interpretable models to provide insights.
result Local interpretable models and autoencoders provide meaningful insights into cancer predictions, identifying distinct and non-generalizable features.
CRITS improves time series classification with interpretable local explanations.
problem Lack of detailed explanations in time series classification models.
method CRITS uses convolutional kernels, max-pooling, and rectified linear units to extract feature weights.
result CRITS provides intrinsically interpretable local explanations without requiring gradients or random perturbations.
Locally adaptive nearest neighbors improve automated systems' performance and are easier to interpret.
problem Improving automated systems' performance and interpretability.
method Developed a method for k nearest neighbors algorithms to learn locally adaptive metrics.
result Locally adaptive metrics improve performance and are interpretable.
IRDs provide local, model-agnostic explanations using hyperboxes.
problem Local model-agnostic explanations for machine learning predictions.
method Formalizes IRDs as hyperboxes, defines optimization problem, introduces unified framework.
result IRDs offer semi-factual explanations and highlight feature importance.
New method identifies important features and interactions in RF models.
problem Limited theoretical understanding of local feature and interaction importance in RF models.
method Combines global and local analysis to identify frequent feature co-occurrences.
result Proves consistent recovery of true local signal features and interactions.
Tree-LIME explains deep learning models using decision trees.
problem Deep learning models are black boxes, making them hard to explain and prone to biases.
method Developed a Tree-LIME approach using decision trees to explain predictions of deep learning models.
result Tree-LIME can capture nonlinear interactions and creates more reliable explanations.
Supervised Machine Learning (SML) algorithms such as Gradient Boosting, Random Forest, and Neural Networks have become popular in recent years due to their increased predictive performance over traditional statistical methods. This is especially true with large data sets (millions or more observations and hundreds to t…
Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to increase the interpretability and explainability of black box Machine Learning (ML) algorithms. LIME typically generates an explanation for a single prediction by any ML model by learning a simpler interpretable model (e.g. linear cla…
Unified view of improving tree model interpretability and debiasing feature importance.
problem Improving interpretability and debiasing feature importance in tree-based models.
method Demonstrates a common thread among bias correction methods and local explanations for trees.
result Points out a bias in explainable AI for trees algorithms due to inbag data inclusion.
Proposes a framework to explain complex global forecasting models.
problem Lack of interpretability in global forecasting models reduces stakeholder trust.
method Trains simpler univariate surrogate models on local forecasts of global models.
result Shows improved local model-agnostic interpretability of global forecasting models.
Local MDI+ improves feature importance for tree-based models, enhancing interpretability and performance.
problem Lack of instance-specific feature importance for tree-based models.
method Local MDI+ extends MDI+ framework to provide instance-specific feature importances.
result Local MDI+ outperforms existing baselines, improving predictive performance by 10%.
Construct geometric interpretation of Heston model using group quantization.
problem Geometric interpretation of Heston model
method Lifted local Lie groupoid formulation
result Geometric interpretation of Heston pricing operator and Riccati equations
We introduce the localized Lasso, which is suited for learning models that are both interpretable and have a high predictive power in problems with high dimensionality d and small sample size n. More specifically, we consider a function defined by local sparse models, one at each data point. We introduce sample-wis…
A new tree-based model for multivariate responses interprets piecewise linear regimes.
problem Recovering piecewise multivariate linear regimes in complex data.
method Twoblock clustering trees with coskewness-based dimension reduction.
result Recovery of piecewise linear regimes in data.
Model-agnostic interpretation methods can mislead if not used carefully.
problem Misinterpretation of machine learning models due to improper use of techniques.
method General pitfalls of model-agnostic interpretation methods.
result Many pitfalls exist when using global interpretation techniques for machine learning models.