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07/10/2023
SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features
The SHAP framework provides a principled method to explain the predictio...
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05/15/2023
Causal Analysis for Robust Interpretability of Neural Networks
Interpreting the inner function of neural networks is crucial for the tr...
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04/13/2023
Task Adaptive Feature Transformation for One-Shot Learning
We introduce a simple non-linear embedding adaptation layer, which is fi...
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11/11/2022
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning
We introduce a family of interpretable machine learning models, with two...
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09/30/2022
Empowering the trustworthiness of ML-based critical systems through engineering activities
This paper reviews the entire engineering process of trustworthy Machine...
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08/09/2022
Explainable prediction of Qcodes for NOTAMs using column generation
A NOtice To AirMen (NOTAM) contains important flight route related infor...
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03/14/2022
FisheyeHDK: Hyperbolic Deformable Kernel Learning for Ultra-Wide Field-of-View Image Recognition
Conventional convolution neural networks (CNNs) trained on narrow Field-...
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12/20/2021
Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions
The potential of reinforcement learning (RL) to deliver aligned and perf...
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02/19/2021
Adaptable Deformable Convolutions for Semantic Segmentation of Fisheye Images in Autonomous Driving Systems
Advanced Driver-Assistance Systems rely heavily on perception tasks such...
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09/29/2020
Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach
Convolutional neural networks (CNNs) are commonly used for image classif...
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06/30/2020
Ontology-guided Semantic Composition for Zero-Shot Learning
Zero-shot learning (ZSL) is a popular research problem that aims at pred...
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06/25/2020
Background Knowledge Injection for Interpretable Sequence Classification
Sequence classification is the supervised learning task of building mode...
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08/13/2019
Local Score Dependent Model Explanation for Time Dependent Covariates
The use of deep neural networks to make high risk decisions creates a ne...
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05/31/2019
Augmenting Transfer Learning with Semantic Reasoning
Transfer learning aims at building robust prediction models by transferr...
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11/13/2018
Interpretable Credit Application Predictions With Counterfactual Explanations
We predict credit applications with off-the-shelf, interchangeable black...
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07/22/2018
Knowledge-based Transfer Learning Explanation
Machine learning explanation can significantly boost machine learning's ...
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05/27/2018
Semantic Explanations of Predictions
The main objective of explanations is to transmit knowledge to humans. T...
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04/24/2017
