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.
PWSHAP provides targeted explanations for complex models.
problem Inability of black-box models to explain targeted effects in sensitive domains.
method Augments model with DAG, uses Shapley values for causal pathway identification.
result Establishes error bounds and demonstrates resolution, interpretability, and locality.
Paper proposes a new method to select memory data for online class-incremental learning.
problem Selecting which buffered images to replay for online class-incremental learning.
method Adversarial Shapley value scoring method to preserve latent decision boundaries.
result Proposed ASER method provides competitive or improved performance compared to state-of-the-art methods.
We develop Neuron Shapley as a new framework to quantify the contribution of individual neurons to the prediction and performance of a deep network. By accounting for interactions across neurons, Neuron Shapley is more effective in identifying important filters compared to common approaches based on activation patterns…
New method calculates Shapley values for uncertain functions.
problem Uncertain value functions in explainable machine learning.
method Definition of Shapley values using probability theory.
result Shapley values can be applied to uncertain functions.
Introduces joint Shapley values to measure feature importance in models.
problem Measuring the importance of feature sets in machine learning models.
method Extends Shapley's axioms to measure a set of features' average contribution to a model's prediction.
result Joint Shapley values provide unique insights and are more consistent with local intuitions.
Shapley value improves model interpretation but not causal inference.
problem Improving model interpretability without losing predictive power.
method Analyzed Shapley value in Bayesian networks, linking it to conditional independence.
result Eliminating high Shapley value variables does not harm predictive performance, but low Shapley value variables can.
Shapley values criticized for feature selection, leading to new insights.
problem Using Shapley values for feature selection is problematic.
method Introduced and critiqued Shapley values as feature selection tools, using counterexamples and simulations.
result Shapley values may not always align with feature selection goals.
The paper introduces Absolute Shapley Value to handle negative contributions in machine learning model training.
problem Negative marginal contributions in machine learning model training.
method Investigates three philosophies: Original Shapley Value, Zero Shapley Value, and Absolute Shapley Value.
result Absolute Shapley Value significantly outperforms other definitions in evaluating data importance.
New insights on Shapley value precision for tabular data predictions.
problem Precision of Shapley value explanations for individual observations.
method Conditional Shapley value estimation methods for tabular data.
result Shapley value explanations are less precise for outer observations.
Unified framework for calculating Shapley values with correlated features.
problem Inaccurate Shapley values due to multicollinearity.
method Unified framework with matrix formulation and mathematical proof.
result Shapley values become independent of feature correlations.
New sampling method estimates Shapley values more accurately.
problem Exponential time complexity of computing Shapley values.
method Multilinear sampling algorithm based on game theory.
result Our method reduces variance and provides more accurate Shapley value estimations.
FastSHAP speeds up Shapley value estimation for black-box models.
problem Efficiently calculating Shapley values for complex models.
method Uses a learned explainer model in a single forward pass.
result Generates high-quality explanations with significant speedup.
New Shapley values reveal non-linear feature dependencies.
problem Understanding non-linear dependencies in machine learning models.
method Model-independent Shapley values using non-parametric measures of dependence.
result Model-independent Shapley values can uncover non-linear dependencies.
Proposes a new method to avoid model extrapolation in Shapley values.
problem Model extrapolation in marginal Shapley values leads to unreliable explanations.
method Proposes a new approach that avoids model extrapolation using marginal averaging and causal information.
result Demonstrates the impacts of model extrapolation on Shapley values and proposes a new method to avoid it.
Paper uses VAEAC to estimate Shapley values for complex models with mixed features.
problem Estimating Shapley values for models with dependent mixed features.
method Uses variational autoencoder with arbitrary conditioning (VAEAC) to model feature dependencies.
result VAEAC approach outperforms state-of-the-art methods for various settings.
This paper proposes a new method to fairly value data in federated learning.
problem Fairly valuing decentralized data contributions in federated learning.
method Variant of Shapley value (federated Shapley value) that is efficient and respects the order of data contributions.
result The federated Shapley value can reflect the real utility of data sources and enhance system robustness, security, and efficiency.
We introduce a variable importance measure to quantify the impact of individual input variables to a black box function. Our measure is based on the Shapley value from cooperative game theory. Many measures of variable importance operate by changing some predictor values with others held fixed, potentially creating unl…
Paper proves Shapley value convergence in Bayesian learning games.
problem Measuring contributions in cooperative games using Bayesian inference.
method Established convergence of Shapley value in parametric Bayesian learning games.
result Shapley value differences converge in probability to a limiting game.
We introduce a new method to explain Gaussian processes using Shapley values.
problem Explaining the uncertainty in Gaussian process models.
method Extending Shapley values to stochastic cooperative games for Gaussian processes.
result Our method generates explanations that are random variables and satisfy favorable axioms.
Study uses Shapley value for sensor anomaly detection, proving its superiority in certain cases.
problem Anomaly detection in sensor networks using the Shapley value.
method Optimized binary classifiers, mathematical proofs for different scenarios.
result Shapley value test can be superior or inferior to single-term tests, depending on sensor correlation and attack type.
The paper explores how Shapley value for a feature can vary based on model outcomes and feature distribution.
problem The uniqueness of Shapley value in explaining model predictions.
method Analyzes the relationship between feature distribution and Shapley value, and compares Shapley values for different model outcomes.
result Shapley value for a feature depends on more than just its mean and can vary significantly based on model outcome.
"How much is my data worth?" is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for instance, fairly distributing profits among multiple data contributors and determining prospective compensation when data breaches happen. In this paper, we study the…
The Shapley value theory is used for risk allocation in non-orthogonal risk factors.
problem Risk allocation among non-orthogonal risk factors in financial portfolios.
method Using Shapley value from cooperative game theory to allocate risk contributions.
result Explicit formulas and numerical algorithms for calculating risk allocations are derived.
Improved Shapley Values for tree-based models, more accurate than existing methods.
problem Inaccurate Shapley Values in tree-based models leading to poor explanations.
method Introduced two new estimators exploiting tree structure, derived correct approach for categorical variables.
result More accurate Shapley Values for tree-based models, demonstrated through simulations.
New methods for estimating conditional Shapley values compared and evaluated.
problem Estimating precise conditional Shapley values for tabular data models.
method Developed new and extended methods using Monte Carlo integration and regression.
result Recommendations for choosing between Monte Carlo and regression methods based on data distribution.
The paper compares LOCO and Shapley values for feature importance, highlighting their limitations and suggesting improvements.
problem Quantifying feature importance in the presence of feature correlation.
method LOCO and Shapley Values, critiquing their axioms and proposing new measures.
result Shapley values do not eliminate feature correlation, and a modified LOCO is recommended.
SIM-Shapley improves SV approximation efficiency and stability.
problem High computational costs of Shapley value methods in high-dimensional settings.
method Stochastic Iterative Momentum for Shapley Value Approximation (SIM-Shapley).
result Reduced computation time by up to 85% while maintaining feature attribution quality.
A method uses Shapley values and Mahalanobis distances to explain multivariate outliers.
problem Explaining multivariate outlyingness in data.
method Decomposing squared Mahalanobis distance using Shapley values.
result Shapley values provide variable contributions to outlying observations.
New sampling methods improve Shapley values for explaining machine learning predictions.
problem Computational limitations in calculating Shapley values for complex models.
method Asymptotic normality results and paired-sampling approximations (KernelSHAP and PermutationSHAP).
result Paired-sampling PermutationSHAP provides exact results for interactions of maximal order two and has the additive recovery property.
This paper proposes an efficient method for calculating Shapley values in Naive Bayes classifiers.
problem The need for explaining machine learning model decisions.
method An exact analytic expression of Shapley values for Naive Bayes classifiers.
result The proposed Shapley values provide informative results with low complexity and low computation time.
ControlSHAP stabilizes Shapley value approximations using control variates.
problem High computational cost of exact Shapley values in blackbox models.
method ControlSHAP uses Monte Carlo control variates to stabilize Shapley value approximations.
result Significant reduction in Monte Carlo variability of Shapley estimates.
We present a method to compute the Shapley values of reconstruction errors of principal component analysis (PCA), which is particularly useful in explaining the results of anomaly detection based on PCA. Because features are usually correlated when PCA-based anomaly detection is applied, care must be taken in computing…
PSI models and infers feature attributions efficiently and accurately.
problem Modeling and inferring feature attributions in flexible predictive models.
method Probabilistic Shapley inference (PSI) framework using latent random variables and a masking-based neural network architecture.
result PSI learns feature attribution distributions centered at Shapley values, revealing meaningful uncertainty.
A new method for efficiently estimating Shapley values in dataset valuation.
problem Quantifying the incremental gain of individual datasets in machine learning tasks.
method Discrete uniform Shapley value approximation.
result Proposes a more efficient method for Shapley value estimation.
Developing an explainable outlier detection method for interval-valued data using Shapley value-based approach.
problem Outlier detection in interval-valued data.
method Proposed a novel approach based on Shapley value for interval-valued data.
result Fine-grained interpretation of outliers with variable contributions.
Paper uses Shapley values to identify key confounders in product funnel data.
problem Identifying important confounders in product funnel data.
method Applies Shapley values for scalable coarsened exact matching.
result Shapley values provide robust importance-ranking for confounders.
A new method simplifies feature explanation for complex models.
problem Explain predictions from complex models efficiently with many features.
method groupShapley: groups features for Shapley value computation.
result Equivalent to summing feature-wise Shapley values within groups.
The problem of explaining the behavior of deep neural networks has recently gained a lot of attention. While several attribution methods have been proposed, most come without strong theoretical foundations, which raises questions about their reliability. On the other hand, the literature on cooperative game theory sugg…
A new sampling scheme based on DOE improves Shapley value estimation accuracy and speed.
problem Heavy computational burden of calculating Shapley values in large coalition games.
method Design of Experiments (DOE) order-of-addition experimental designs for sampling.
result DOE-based sampling scheme yields more accurate and sometimes deterministic estimates of Shapley values.
Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual predictions from a complex machine learning model by learning simple, interpretable explanations. Shapley values is a game theoretic concept that can be used for this purpose. The Shapley valu…
RKHS-SHAP uses Shapley values for kernel methods to provide feature attributions.
problem Feature attribution for kernel methods is often heuristic and not individualised.
method RKHS-SHAP uses Shapley values from coalition game theory to compute feature attributions for kernel machines efficiently.
result RKHS-SHAP can compute both Interventional and Observational Shapley values.
Mathematical proofs for Shapley explanations clarify their properties.
problem Clarify the essential axioms and properties of Shapley values in machine learning.
method Mathematical rigor and axiomatic characterization.
result The symmetry axiom is essential for Shapley values, contradicting Lundberg and Lee's claim.
Paper proposes using tree-based surrogate models for efficient Shapley computation.
problem Efficient computation of Shapley values using conditional expectations.
method Surrogate model-based tree for approximating Shapley and SHAP values.
result The proposed algorithm improves accuracy and unifies global interpretation.
This research adapts superpixels for Shapley value computation in DNA profile classification.
problem Efficiently computing Shapley values for large, multidimensional time-series data.
method Adapting the concept of superpixels to streamline Shapley value computation for time-series-like data.
result Realistic, accurate, and fast computation of Shapley values for DNA profile classification.
Unified and noise-reduced data valuation framework for machine learning.
problem Quantifying the contribution of individual data points in machine learning.
method Beta Shapley, a generalization of Data Shapley, relaxes the efficiency axiom.
result Beta Shapley outperforms state-of-the-art data valuation methods on various ML tasks.
Bayesian approach improves Shapley value estimation efficiency.
problem Efficiently estimating Shapley values in machine learning models.
method Bayesian experimental design using Gaussian process surrogate and adaptive coalition selection.
result Consistently improves sample efficiency in low-budget settings.
New local MDI variable importances derived from global scores match Shapley values.
problem Local feature relevance in tree-based models.
method Deriving local MDI importance measure from global scores and linking it to Shapley values.
result Local MDI importances have a natural connection with Shapley values.