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.
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.
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.
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.
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.
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.
Shapley Flow interprets model predictions using a graph-based approach to feature importance.
problem Existing feature importance methods ignore or hide feature dependencies.
method Shapley Flow considers the entire causal graph and assigns credit to edges.
result Shapley Flow provides a deeper, graph-based view of feature importance.
Group Shapley evaluates feature groups in business data, improving explainability in AI.
problem Evaluating the importance of feature groups in business and economic data.
method Developed Group Shapley and a significance testing procedure based on chi-square approximation.
result Market-related variables are identified as the most influential feature group.
Pref-SHAP explains preferences using Shapley values.
problem Challenging problem of preference explanation in machine learning.
method Shapley value-based model explanation framework for pairwise comparison data.
result Richer and more insightful explanations obtained over baseline.
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…
The study compares different game-theoretic attribution methods and finds that interventional Shapley values yield less consistent results than Aumann-Shapley due to path symmetry.
problem Investigating the influence of path choice on game-theoretic attribution algorithms.
method Comparative analysis of interventional Shapley values and Generalized Integrated Gradients (GIG) methods.
result Interventional Shapley values yield less consistent attributions than Aumann-Shapley due to path symmetry and extended away from the training data manifold.
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.
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 explores alternative cooperative game theory methods for machine learning feature attribution.
problem Debate over Shapley values' relevance in feature attribution.
method Introduces Weber and Harsanyi sets as alternative allocation schemes.
result Provides a coherent framework for designing robust feature attributions.
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.
Develops fast approximations for conditional Shapley values in linear and polynomial models.
problem Estimating conditional Shapley values using regression models is computationally expensive.
method A new approximative estimation method for conditional Shapley values using linear and polynomial regression models.
result Our method significantly reduces computation time compared to existing methods.
Paper improves KNN-Shapley for privacy-friendly data valuation.
problem Privacy challenges in data valuation methods.
method Introduces TKNN-Shapley, a privacy-friendly variant of KNN-Shapley.
result TKNN-Shapley offers superior privacy-utility tradeoff compared to naively privatized KNN-Shapley.
Paper uses Shapley values to explain how clusters of training data affect predictions.
problem Explaining how training data clusters impact predictions from black-box models.
method Extends Shapley values to cluster importance, using coalitional game theory.
result Shows how different clusters of training data contribute to model predictions.
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.
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.
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.
New method attributes feature uncertainty in ML models using cooperative game theory.
problem Lack of feature-level uncertainty attribution in explainable AI.
method Proposes a novel, model-agnostic uncertainty attribution method using cooperative game theory and conformal prediction.
result Demonstrates improved runtime efficiency and practical utility in real-world applications.
SurvFD and SurvSHAP-IQ provide interpretable survival models by analyzing feature interactions.
problem Non-additivity of hazard and survival functions limits standard additive explanation methods.
method SurvFD decomposes higher-order effects into time-dependent and time-independent components, extending Shapley interactions to time-indexed functions.
result SurvFD and SurvSHAP-IQ offer a new perspective on survival explanations, explicitly characterizing feature interactions.
A new method explains mixed features for predictive models using conditional inference trees.
problem Explaining complex machine learning models with mixed features.
method Proposes a method to explain mixed features (continuous, discrete, ordinal, categorical) using conditional inference trees.
result Our method often outperforms current industry standards in various simulation studies and real-world financial data.
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.
Efficient WKNN-Shapley computation improves data valuation accuracy.
problem Efficient computation of Data Shapley for WKNN algorithm.
method Reframed WKNN-Shapley as a counting problem, introduced quadratic-time algorithm.
result Quadratic-time WKNN-Shapley computation, improving from O(NK). Study reveals Data Shapley's inconsistent performance in data selection tasks.
problem Inconsistency of Data Shapley's performance in data selection across different settings.
method Hypothesis testing framework and identification of utility functions.
result Data Shapley's performance is no better than random selection without specific constraints.
A new method to explain black box models using Shapley values.
problem Quantifying the impact of individual input variables in black box functions.
method Cohort Shapley measure based on cooperative game theory, using similarity cohorts.
result Introduces a new squared cohort Shapley value for variable importance.
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.
Develops Shapley explainability solutions respecting data manifold.
problem Tenable assumption of uncorrelated features in Shapley explainability.
method Two solutions: generative modelling and direct learning of Shapley value-function.
result On-manifold Shapley explainability overcomes drawbacks of 'off-manifold' values.
A new Shapley value approach for neural networks interpretable and stable.
problem Neural networks' interpretability and training stability issues.
method Shapley value approximation for ReLU activation, globally continuous Shapley gradient, Shapley Activation function.
result SA consistently outperforms ReLU in training convergence, accuracy, and stability.
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.
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.
SHAFF estimates Shapley effects efficiently even with dependent variables.
problem Challenges in estimating Shapley effects, especially with dependent variables.
method SHAFF uses random forests to estimate Shapley effects efficiently.
result SHAFF provides a fast and accurate estimate of Shapley effects.
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 method for interpreting financial model risks.
problem Fairly allocating risk in financial models.
method Extending Shapley value framework for axiomatic risk attribution.
result Risk can be well allocated in financial models.
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.
Neuron Shapley identifies key neurons in deep networks, improving model accuracy and fairness.
problem Identifying responsible neurons in deep networks for better model performance and fairness.
method Neuron Shapley framework quantifies neuron contributions, accounting for interactions.
result Removing just 30 critical filters can destroy model accuracy, revealing network function.
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.
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.
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.
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 introduces Shapley curves for measuring variable importance in nonparametric settings.
problem Limited statistical understanding of Shapley values as variable importance measures.
method Introduces Shapley curves based on conditional expectation and covariate distribution; derives convergence rates and normality; proposes a novel bootstrap procedure.
result Validates theoretical findings with numerical studies and analyzes vehicle prices determinants.
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.
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.
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.
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.
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.