New tools explain FRF model predictions in high-dimensional ECG data.
problem Lack of interpretability in Functional Random Forests (FRF) models.
method Introduces FPDPs, FPC Probability Heatmaps, and various FPC importance metrics.
result Enhances transparency of FRF models by revealing FPC contributions.
New method explains high-dimensional text classifiers.
problem Limited explainability tools for high-dimensional inputs and neural networks.
method Theoretical high-dimensional properties in neural networks.
result Improved explainability for neural network classifiers.
XLabel tool reduces medical experts' workload by 40% and explains its decisions.
problem Efficiently labeling large electronic health records.
method Visual-interactive tool using Explainable Boosting Machine (EBM) for classification and explanation.
result EBM achieves high accuracy and explainability, even with mislabeled data.
PySS3 simplifies access to SS3's text classification and visualization.
problem Limited availability of an open-source SS3 implementation.
method Developed PySS3, an open-source Python package implementing SS3.
result PySS3 enables robust, explainable, and trusty text classification.
The paper uses statistics to improve the explainability of models.
problem Subjective human assessment of explanations and lack of theoretical guarantees.
method Leveraging statistical estimators for proper definition and evaluation of explanations.
result Statistical tools provide theoretical guarantees and evaluation metrics for explanations.
Enhances interpretability of functional survival models.
problem Lack of interpretability in functional survival models limits practical use.
method Introduces novel methods to enhance interpretability of FST and explainability of FRSF.
result Proposed methods yield efficient, easy-to-understand decision trees.
Proposes counterfactual explainability for causal attribution, extending variance analysis methods.
problem Lack of mechanistic understanding in existing tools for explaining complex models.
method Extends global sensitivity analysis methods to causal explanations using directed acyclic graphs.
result Developed methods to estimate counterfactual explainability and applied to income inequality analysis.
This paper catalogs R packages for explaining AI models.
problem Ensuring safe and effective functioning of predictive models.
method Taxonomy of model explanations and comparison of R packages.
result 27 R packages for XAI identified and compared.
survex explains machine learning survival models, improving model transparency.
problem Lack of tools to explain machine learning survival models.
method Introduces survex R package using explainable AI techniques.
result Improves model reliability and detects biases in survival models.
Explains algebraic tools for understanding invariant differential operators in curved geometries.
problem Understanding invariant differential operators in curved geometries.
method Focuses on the direct link between jets of sections and induced modules.
result Extends concepts and procedures to curved cases.
I present in this paper some tools in Symplectic and Poisson Geometry in view of their applications in Geometric mechanics and Mathematical Physics. After a short discussion of the Lagrangian and Hamiltonian formalisms, including the use of symmetry groups, and a presentation of the Tulczyjew's isomorphisms (which expl…
Unified framework for explaining models by removing features.
problem Unclear relationships and preferences among existing model explanation methods.
method Removal-based explanations characterized by three dimensions.
result Unified framework unifies 26 existing methods.
Survey connects XAI and surrogate modeling for better understanding of complex systems.
problem Opaque surrogate models hide input-output relationships, hindering decision-making.
method Synthesizes XAI techniques with surrogate modeling workflows.
result Surrogate models can be made more interpretable using XAI methods.
Book covers tools for zeroth-order convex optimisation.
problem Zeroth-order convex optimisation.
method Cutting plane methods, interior point methods, continuous exponential weights, gradient descent, online Newton step.
result Improved existing bounds and algorithms.
AxNN improves model interpretability without sacrificing predictive power.
problem Black-box nature of machine learning models hinders interpretation and explanation.
method AxNN combines ensembles of generalized additive model networks and additive index models.
result AxNN achieves both good predictive performance and model interpretability.
Persistence norms explain financial uncertainty better than volatility.
problem Capturing financial instability and predictability.
method Applied topological data analysis to financial markets.
result Persistence norms are significant in explaining financial uncertainty, while volatility is less effective.
LLMs can help explain credit risk models but not autonomously.
problem Leveraging LLMs for post-hoc explainability in credit risk models.
method Comparison of LLM outputs with SHAP and coefficient-based attributions on three LMs.
result LLMs reliably preserve feature-importance rankings but poorly align with autonomous explanations.
The paper uses XAI to predict RFQ fulfillment accuracy.
problem Improving accuracy in predicting RFQ fulfillment for less liquid asset classes.
method Advanced algorithms like Logistic Regression, Random Forest, XGBoost, and Bayesian Neural Tree.
result Improved accuracy in RFQ fill rate predictions.
Focuses on monitoring and explaining models in real-world applications.
problem Ensuring high quality machine learning services in production environments.
method Statistical techniques for model performance and data monitoring, explanations of predictions.
result Challenges and solutions for implementing monitoring and explanation in production models.
ALMANACS benchmarks explainability methods on simulatability.
problem Evaluating the effectiveness of explainability methods for language models.
method ALMANACS is a simulatability benchmark that evaluates explainability methods on twelve safety-relevant topics.
result No explainability method outperforms the explanation-free control across all topics.
Explains the difference between EMA and moving EMA, focusing on market trend indicators.
problem Understanding the difference between exponential moving average and moving exponential average.
method Explains the mathematical tools and definitions of trend indicators.
result Discusses the properties of the MACD indicator and its use in market trend analysis.
New approach to explain fairness in machine learning models.
problem Detect, understand, and mitigate unfairness in machine learning models.
method Shapley value paradigm and meta algorithm for training-time fairness interventions.
result Meta algorithm provides insight into accuracy-fairness trade-off.
SMT-EX enhances SMT for explaining surrogate models of mixed-variable design problems.
problem Making decisions and understanding complex systems using surrogate models of mixed-variable design problems.
method Integrates explainability techniques into SMT, including Shapley Additive Explanations, Partial Dependence Plot, and Individual Conditional Expectations.
result Demonstrates versatility in addressing diverse problem characteristics.
This guide simplifies explainable deep learning for beginners.
problem Difficulty in understanding deep learning model decisions.
method Introduces three dimensions for explainable deep learning methods.
result Clarifies evaluations for model explanations.
We introduce the historical development and physical idea behind topological Yang-Mills theory and explain how a physical framework describing subatomic physics can be used as a tool to study differential geometry. Further, we emphasize that this phenomenon demonstrates that the interrelation between physics and mathem…
Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of the models makes them hard for the data analyst to interpret the results and explain them without additional tools. This has led to much rese…
Improves global counterfactual explanations for model recourse.
problem Inability to provide explanations beyond local instances.
method Investigates and improves Actionable Recourse Summaries (AReS) for global counterfactual explanations.
result Develops more efficient and interactive explainability tools.
MARLeME extracts MARL models into symbolic models for better interpretability.
problem Improving interpretability and explainability of MARL systems.
method Develops a MARL model extraction library that approximates MARL systems with symbolic models.
result Enhanced understanding and inspection of MARL systems and agents.
Archetypal analysis helps understand binary data sets.
problem Explaining binary questionnaire data.
method Using archetypal analysis for binary observations.
result The approach contributes to understanding binary data sets.
Paper compares AI models for credit scoring and explains them.
problem Lack of interpretability in advanced AI models hinders credit risk management.
method Comparison of logistic regression, AI algorithms, and techniques to interpret AI models.
result Advanced tree-based models provide the best prediction of client default.
The paper introduces a method to assess the reliability of model explanations.
problem Assessing the quality and reliability of model explanations.
method An Ordinal Consensus Approach using diverse bootstrapped surrogate explainers.
result Uncertainty estimates offer actionable insights beyond standard surrogate explainers.
This paper extends explainability methods to non-Gaussian Gaussian Processes.
problem Making non-Gaussian GP models transparent and explainable.
method Proposes Integrated Gradient-based explainability for non-Gaussian GP models.
result Offers both analytical and approximate solutions for non-Gaussian GP models.
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.
Unified framework for model explanation methods based on feature removal.
problem Unclear relationships and preferences among various model explanation methods.
method Characterizes removal-based explanations along three dimensions.
result Unified 26 existing methods, including widely used approaches.
XAI-Bench releases synthetic datasets for evaluating feature attribution methods.
problem Evaluating and comparing feature attribution methods is challenging.
method Released synthetic datasets and benchmarking library.
result Efficiently evaluates feature attribution methods across various metrics.
Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are the most popular non-linear predictive models used in practice today, yet comparatively little attention has been paid to explaining their predictions. Here we significantly improve the interpretability of tree-bas…
After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted th…
New framework explains deep learning using signal processing techniques.
problem Lack of a rigorous mathematical theory explaining deep learning performance.
method Transform-domain sparse regularization, Radon transform, and approximation theory.
result Explains neural network properties and performance.
Deep learning generalizes well despite being overparameterized.
problem Why deep networks generalize well despite fitting training data perfectly.
method Empirical study of training methods and derivation of data-dependent generalization bounds.
result Uniform convergence alone is insufficient for explaining generalization in overparameterized settings.
RFX-Fuse combines Breiman and Cutler's Random Forest with modern ML capabilities.
problem Lack of a unified ML engine with diverse capabilities.
method Unified ML engine with native GPU/CPU support, delivering 5+ functionalities in one model.
result Native explainable similarity and imputation validation.
A review of ML and DL for ecological data analysis.
problem Understanding the strengths and limitations of ML and DL in ecological research.
method Historical overview, algorithm families, differences, universal principles, and emerging trends.
result ML and DL excel in prediction tasks but are still debated for causal inference.
This paper introduces a new classification tool named Silas, which is built to provide a more transparent and dependable data analytics service. A focus of Silas is on providing a formal foundation of decision trees in order to support logical analysis and verification of learned prediction models. This paper describes…
This survey reviews portfolio choice in settings where investment opportunities are stochastic due to, e.g., stochastic volatility or return predictability. It is explained how to heuristically compute candidate optimal portfolios using tools from stochastic control, and how to rigorously verify their optimality by mea…
The paper uses Cartan moving frames to analyze data manifolds and neural network outputs.
problem Understanding the geometry and explainability of neural network outputs.
method Employing Cartan moving frames to study the Riemannian structure of data manifolds and their curvature.
result The relationship between neural network outputs and the geometry of inputs is exploited for explainable AI.
MCPCA analyzes shared factors across multiple data contexts.
problem No tools to recover shared factors across multiple contexts.
method Developed a theoretical and algorithmic framework (MCPCA).
result Reveals shared axes of variation across subsets of contexts.
TIR expands LLM capabilities by enabling problem-solving strategies.
problem Lack of a principled theory explaining why LLMs with tools are more capable.
method Formal proof and Advantage Shaping Policy Optimization (ASPO) algorithm.
result TIR model decisively outperforms pure-text models on challenging benchmarks.
This paper aims to analyze knowledge consistency between pre-trained deep neural networks. We propose a generic definition for knowledge consistency between neural networks at different fuzziness levels. A task-agnostic method is designed to disentangle feature components, which represent the consistent knowledge, from…
Proposes SGShift to identify shifted features causing model performance degradation under concept shift.
problem Concept shift leading to miscalibration in ML models across domains.
method SGShift method for identifying sparse set of shifted features using feature selection and statistical tools.
result SGShift identifies shifted features more accurately than baseline methods, requires few samples in the shifted domain, and is robust to complex cases.