SOGAR learns Pareto-optimal recourse summaries by solving a bi-objective decision tree problem, yielding stable low-cost effective group actions that outperform prior methods on effectiveness and cost.
citation dossier
A Unified Approach to Interpreting Model Predic- tions, November 2017
18Pith papers citing it
19reference links
cs.LGtop field · 4 papers
UNVERDICTEDtop verdict bucket · 17 papers
why this work matters in Pith
Pith has found this work in 18 reviewed papers. Its strongest current cluster is cs.LG (4 papers). The largest review-status bucket among citing papers is UNVERDICTED (17 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
fields
cs.LG 4 cs.AI 3 cs.CR 2 cs.SE 2 astro-ph.GA 1 astro-ph.SR 1 cs.CL 1 cs.CV 1 cs.HC 1 eess.IV 1years
2026 18representative citing papers
Reinforcement learning on MIR features combined with cargo-fuzz validation reduces false positives in Rust static memory safety analysis, raising precision from 25.6% to 59.0% and accuracy to 65.2%.
The paper presents a taxonomy of seven production-specific failure modes for agentic AI, demonstrates that existing metrics fail to detect four of them entirely, and proposes the PAEF five-dimension framework for continuous production evaluation with an open-source implementation.
A new scale-aware diagnostic framework shows that unconstrained diffusion generative models exhibit structural freezing and instability instead of smooth physical responses under multiscale perturbations.
Cross-modal averaging maps ECG model attributions to CineECG 3D space, raising Dice overlap with expert annotations from 0.47 to 0.56 on 20 cases while filtering attribution noise.
TrajOnco uses a chain-of-agents LLM architecture with memory to perform temporal reasoning on longitudinal EHR, achieving 0.64-0.80 AUROC for 1-year multi-cancer risk prediction in zero-shot mode on matched cohorts while matching supervised ML on lung cancer and outperforming single-agent baselines.
Gradient boosting produces risk scores with competitive accuracy but 60% fewer rules on classification tasks and 16% fewer on time-to-event tasks than regression-based methods like AutoScore.
A framework unifies multimodal intent interpretation, interaction-centric explainability, and agency-preserving controls as interdependent requirements for trustworthy Human-AI collaboration.
Cognitive models of user reasoning strategies with XAI methods on tabular data fit human forward-simulation decisions better than ML baselines and support hypothesis testing without new user studies.
A new sample of young candidate Bridge stars is identified and shown to align with gas structures, with kinematics implying a ~125 Myr crossing time consistent with the last LMC-SMC interaction.
A blockchain-anchored explainable ML system delivers tamper-evident fraud detection with F1 of 0.895 and sub-25ms latency on Layer-2 networks.
A visual transformer model trained on IRIS inversions predicts chromospheric temperature and density from SDO data with correlations around 0.8 on 80% of test cases.
A surrogate modeling method approximates LLM-encoded medical knowledge via prompting to quantify variable influence and flag inaccuracies and racial biases.
An optimized KernelSHAP method for 3D medical image segmentation restricts computation to ROI and receptive fields, uses patch logit caching for 15-30% savings, and compares organ units versus supervoxels for clinically interpretable attributions.
A conditional Wasserstein GAN generates plausible future SWI drought trajectories for French insurance risk management under climate change.
LightGBM with team-level features outperforms a bank's existing rule-based change risk process on a one-year dataset while using SHAP for regulatory explainability.
SDNGuardStack ensemble learning model reports 99.98% accuracy and 0.9998 Cohen's kappa on the InSDN dataset for SDN intrusion detection while providing SHAP-based explanations.
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.
citing papers explorer
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Optimal Recourse Summaries via Bi-Objective Decision Tree Learning
SOGAR learns Pareto-optimal recourse summaries by solving a bi-objective decision tree problem, yielding stable low-cost effective group actions that outperform prior methods on effectiveness and cost.
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Mitigating False Positives in Static Memory Safety Analysis of Rust Programs via Reinforcement Learning
Reinforcement learning on MIR features combined with cargo-fuzz validation reduces false positives in Rust static memory safety analysis, raising precision from 25.6% to 59.0% and accuracy to 65.2%.
-
Evaluating Agentic AI in the Wild: Failure Modes, Drift Patterns, and a Production Evaluation Framework
The paper presents a taxonomy of seven production-specific failure modes for agentic AI, demonstrates that existing metrics fail to detect four of them entirely, and proposes the PAEF five-dimension framework for continuous production evaluation with an open-source implementation.
-
Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems
A new scale-aware diagnostic framework shows that unconstrained diffusion generative models exhibit structural freezing and instability instead of smooth physical responses under multiscale perturbations.
-
Validating the Clinical Utility of CineECG 3D Reconstructions through Cross-Modal Feature Attribution
Cross-modal averaging maps ECG model attributions to CineECG 3D space, raising Dice overlap with expert annotations from 0.47 to 0.56 on 20 cases while filtering attribution noise.
-
TrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection
TrajOnco uses a chain-of-agents LLM architecture with memory to perform temporal reasoning on longitudinal EHR, achieving 0.64-0.80 AUROC for 1-year multi-cancer risk prediction in zero-shot mode on matched cohorts while matching supervised ML on lung cancer and outperforming single-agent baselines.
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Gradient Boosted Risk Scores
Gradient boosting produces risk scores with competitive accuracy but 60% fewer rules on classification tasks and 16% fewer on time-to-event tasks than regression-based methods like AutoScore.
-
Toward a Unified Framework for Collaborative Design of Human-AI Interaction
A framework unifies multimodal intent interpretation, interaction-centric explainability, and agency-preserving controls as interdependent requirements for trustworthy Human-AI collaboration.
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CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations
Cognitive models of user reasoning strategies with XAI methods on tabular data fit human forward-simulation decisions better than ML baselines and support hypothesis testing without new user studies.
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Characterisation of the Clouds' young stellar Bridge using Gaia DR3
A new sample of young candidate Bridge stars is identified and shown to align with gas structures, with kinematics implying a ~125 Myr crossing time consistent with the last LMC-SMC interaction.
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Who Audits the Auditor? Tamper-Proof Fraud Detection with Blockchain-Anchored Explainable ML
A blockchain-anchored explainable ML system delivers tamper-evident fraud detection with F1 of 0.895 and sub-25ms latency on Layer-2 networks.
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Predicting the thermodynamics in the chromosphere from the translation of SDO data into the IRIS$^{2}$ inversion results using a visual transformer model
A visual transformer model trained on IRIS inversions predicts chromospheric temperature and density from SDO data with correlations around 0.8 on 80% of test cases.
-
Surrogate modeling for interpreting black-box LLMs in medical predictions
A surrogate modeling method approximates LLM-encoded medical knowledge via prompting to quantify variable influence and flag inaccuracies and racial biases.
-
Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation
An optimized KernelSHAP method for 3D medical image segmentation restricts computation to ROI and receptive fields, uses patch logit caching for 15-30% savings, and compares organ units versus supervoxels for clinically interpretable attributions.
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A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence
A conditional Wasserstein GAN generates plausible future SWI drought trajectories for French insurance risk management under climate change.
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Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
LightGBM with team-level features outperforms a bank's existing rule-based change risk process on a one-year dataset while using SHAP for regulatory explainability.
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SDNGuardStack: An Explainable Ensemble Learning Framework for High-Accuracy Intrusion Detection in Software-Defined Networks
SDNGuardStack ensemble learning model reports 99.98% accuracy and 0.9998 Cohen's kappa on the InSDN dataset for SDN intrusion detection while providing SHAP-based explanations.
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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.