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Found 15 papers, 4 papers with code
Quantifying Prediction Consistency Under Model Multiplicity in Tabular LLMs
This work formalizes the challenge of fine-tuning multiplicity in Tabular LLMs and proposes a novel metric to quantify the robustness of individual predictions without expensive model retraining.
Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models
To achieve effective data scaling, we aim to develop a general model that is able to capture diverse data patterns of graphs and can be utilized to adaptively help the downstream tasks.
Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions
This paper proposes Progressive Inference - a framework to compute input attributions to explain the predictions of decoder-only sequence classification models.
Counterfactual Metarules for Local and Global Recourse
We introduce T-CREx, a novel model-agnostic method for local and global counterfactual explanation (CE), which summarises recourse options for both individuals and groups in the form of human-readable rules.
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations
Explainable Artificial Intelligence (XAI) has received widespread interest in recent years, and two of the most popular types of explanations are feature attributions, and counterfactual explanations.
GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations
Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods prominent in fairness, recourse and model understanding.
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
There is an emerging interest in generating robust counterfactual explanations that would remain valid if the model is updated or changed even slightly.
Symbolic Metamodels for Interpreting Black-boxes Using Primitive Functions
This composition can be represented in the form of a tree.
CLEAR: Generative Counterfactual Explanations on Graphs
Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted label?".
Robust Counterfactual Explanations for Tree-Based Ensembles
In this work, we propose a novel strategy -- that we call RobX -- to generate robust counterfactuals for tree-based ensembles, e. g., XGBoost.
Global Counterfactual Explanations: Investigations, Implementations and Improvements
Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding.
A Survey on the Robustness of Feature Importance and Counterfactual Explanations
There exist several methods that aim to address the crucial task of understanding the behaviour of AI/ML models.
Interpreting Black-boxes Using Primitive Parameterized Functions
One approach for interpreting black-box machine learning models is to find a global approximation of the model using simple interpretable functions, which is called a metamodel (a model of the model).
Reliable Local Explanations for Machine Listening
One way to analyse the behaviour of machine learning models is through local explanations that highlight input features that maximally influence model predictions.
GAN-based Generation and Automatic Selection of Explanations for Neural Networks
However, this requires a careful selection of hyper-parameters to generate interpretable examples for each neuron of interest, and current methods rely on a manual, qualitative evaluation of each setting, which is prohibitively slow.
