research
∙
07/17/2023
Towards Accelerating Benders Decomposition via Reinforcement Learning Surrogate Models
Stochastic optimization (SO) attempts to offer optimal decisions in the ...
share
research
∙
07/13/2023
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations
Explainable Artificial Intelligence (XAI) has received widespread intere...
research
∙
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...
research
∙
05/26/2023
GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations
Counterfactual explanations have been widely studied in explainability, ...
research
∙
05/19/2023
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
There is an emerging interest in generating robust counterfactual explan...
research
∙
01/21/2023
Bayesian Hierarchical Models for Counterfactual Estimation
Counterfactual explanations utilize feature perturbations to analyze the...
research
∙
11/21/2022
Learn to explain yourself, when you can: Equipping Concept Bottleneck Models with the ability to abstain on their concept predictions
The Concept Bottleneck Models (CBMs) of Koh et al. [2020] provide a mean...
research
∙
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...
research
∙
11/07/2022
Towards learning to explain with concept bottleneck models: mitigating information leakage
Concept bottleneck models perform classification by first predicting whi...
research
∙
10/05/2022
Feature Importance for Time Series Data: Improving KernelSHAP
Feature importance techniques have enjoyed widespread attention in the e...
research
∙
07/06/2022
Robust Counterfactual Explanations for Tree-Based Ensembles
Counterfactual explanations inform ways to achieve a desired outcome fro...
research
∙
04/14/2022
Global Counterfactual Explanations: Investigations, Implementations and Improvements
Counterfactual explanations have been widely studied in explainability, ...
research
∙
03/23/2022
Asynchronous Collaborative Learning Across Data Silos
Machine learning algorithms can perform well when trained on large datas...
research
∙
03/16/2022
Explaining Preference-driven Schedules: the EXPRES Framework
Scheduling is the task of assigning a set of scarce resources distribute...
research
∙
03/14/2022
Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction
We consider a novel queuing problem where the decision-maker must choose...
research
∙
10/30/2021
A Survey on the Robustness of Feature Importance and Counterfactual Explanations
There exist several methods that aim to address the crucial task of unde...
research
∙
10/27/2021
Counterfactual Shapley Additive Explanations
Feature attributions are a common paradigm for model explanations due to...
research
∙
09/25/2021
Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification
This paper presents an end-to-end system for fact extraction and verific...
research
∙
06/29/2021
Counterfactual Explanations for Arbitrary Regression Models
We present a new method for counterfactual explanations (CFEs) based on ...
research
∙
03/29/2021
Contrastive Explanations of Plans Through Model Restrictions
In automated planning, the need for explanations arises when there is a ...
research
∙
03/20/2020
Robust Plan Execution with Unexpected Observations
In order to ensure the robust actuation of a plan, execution must be ada...
research
∙
11/17/2019
Towards Efficient Anytime Computation and Execution of Decoupled Robustness Envelopes for Temporal Plans
One of the major limitations for the employment of model-based planning ...
research
∙
08/14/2019
Towards Explainable AI Planning as a Service
Explainable AI is an important area of research within which Explainable...
research
∙
10/15/2018
Towards Providing Explanations for AI Planner Decisions
In order to engender trust in AI, humans must understand what an AI syst...
research
∙
07/11/2018
Explainable Security
The Defense Advanced Research Projects Agency (DARPA) recently launched ...
research
∙
09/29/2017
Explainable Planning
As AI is increasingly being adopted into application solutions, the chal...
research
∙
04/12/2017
CASP Solutions for Planning in Hybrid Domains
CASP is an extension of ASP that allows for numerical constraints to be ...
research
∙
08/31/2016
PDDL+ Planning via Constraint Answer Set Programming
PDDL+ is an extension of PDDL that enables modelling planning domains wi...
research
∙
01/23/2014