2 citations · 6 across the 9 of their papers we have counts for
10 papers
GRAIL: From Saliency to Semantics in Time-Series Explanation
Enver Menadjiev, Yiqiu Shen, Jaesik Choi +1
Explaining a time-series model requires both locating the evidence and extracting meaningful knowledge from it. Yet a single timestep is not a semantic unit: the meaningful pattern…
RelShap: Relationally Consistent Shapley Explanations
Seungeun Lee, Joao Fonseca, Julia Stoyanovich
Machine learning pipelines commonly flatten relational data into single-table representations, discarding structural constraints. Widely used Shapley value-based feature attributio…
ExplainerPFN: Towards tabular foundation models for model-free zero-shot feature importance estimations
Joao Fonseca, Julia Stoyanovich
Computing the importance of features in supervised classification tasks is critical for model interpretability. Shapley values are a widely used approach for explaining model predi…
VirnyFlow: Optimizing ML Pipelines for Accuracy, Fairness, and Stability at Scale
Denys Herasymuk, Anastasiia Mozghova, Nazar Protsiv +2
Developing machine learning (ML) systems for real-world deployment requires navigating context-dependent trade-offs among accuracy, fairness, stability, and other objectives. Exist…
SHAP-based Explanations are Sensitive to Feature Representation
Hyunseung Hwang, Andrew Bell, Joao Fonseca +3
Local feature-based explanations are a key component of the XAI toolkit. These explanations compute feature importance values relative to an ``interpretable'' feature representatio…
Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offs
Joao Fonseca, Andrew Bell, Julia Stoyanovich
Large Language Models (LLMs) have been shown to be susceptible to jailbreak attacks, or adversarial attacks used to illicit high risk behavior from a model. Jailbreaks have been ex…
Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice
Andrew Bell, Julia Stoyanovich
Concerns about the risks and harms posed by artificial intelligence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Expla…
Table2Image: Lightweight Tabular Learning with Generated Proxy Representations and Reliability Diagnostics
Seungeun Lee, Kihwan Lee, Subin Bae +5
Deep tabular models should ideally balance predictive performance, parameter efficiency, and robustness to imperfect learning signals---properties that are rarely considered jointl…
Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation
Falaah Arif Khan, Denys Herasymuk, Nazar Protsiv +1
Data missingness is a practical challenge of sustained interest to the scientific community. In this paper, we present Shades-of-Null, an evaluation suite for responsible missing v…
ShaRP: Explaining Rankings and Preferences with Shapley Values
Venetia Pliatsika, Joao Fonseca, Kateryna Akhynko +2
Algorithmic decisions in critical domains such as hiring, college admissions, and lending are often based on rankings. Given the impact of these decisions on individuals, organizat…