research
∙
03/29/2023
Fairness-Aware Data Valuation for Supervised Learning
Data valuation is a ML field that studies the value of training instance...
share
research
∙
02/15/2023
A Case Study on Designing Evaluations of ML Explanations with Simulated User Studies
When conducting user studies to ascertain the usefulness of model explan...
research
∙
11/24/2022
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
Evaluating new techniques on realistic datasets plays a crucial role in ...
research
∙
10/25/2022
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering
Anti-money laundering (AML) regulations mandate financial institutions t...
research
∙
09/16/2022
FairGBM: Gradient Boosting with Fairness Constraints
Machine Learning (ML) algorithms based on gradient boosted decision tree...
research
∙
07/13/2022
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions
In recent years, machine learning algorithms have become ubiquitous in a...
research
∙
06/27/2022
Human-AI Collaboration in Decision-Making: Beyond Learning to Defer
Human-AI collaboration (HAIC) in decision-making aims to create synergis...
research
∙
06/27/2022
Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction
The unparalleled ability of machine learning algorithms to learn pattern...
research
∙
06/24/2022
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods
Machine Learning (ML) models now inform a wide range of human decisions,...
research
∙
05/07/2022
ConceptDistil: Model-Agnostic Distillation of Concept Explanations
Concept-based explanations aims to fill the model interpretability gap f...
research
∙
04/26/2021
Weakly Supervised Multi-task Learning for Concept-based Explainability
In ML-aided decision-making tasks, such as fraud detection or medical di...
research
∙
03/23/2021
Promoting Fairness through Hyperparameter Optimization
Considerable research effort has been guided towards algorithmic fairnes...
research
∙
01/21/2021
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations
There have been several research works proposing new Explainable AI (XAI...
research
∙
11/30/2020
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations
Recurrent neural networks are a standard building block in numerous mach...
research
∙
11/27/2020
Teaching the Machine to Explain Itself using Domain Knowledge
Machine Learning (ML) has been increasingly used to aid humans to make b...
research
∙
10/07/2020
A Bandit-Based Algorithm for Fairness-Aware Hyperparameter Optimization
Considerable research effort has been guided towards algorithmic fairnes...
research
∙
11/14/2018
Aequitas: A Bias and Fairness Audit Toolkit
Recent work has raised concerns on the risk of unintended bias in algori...
research
∙
10/13/2018
Social Media Brand Engagement as a Proxy for E-commerce Activities: A Case Study of Sina Weibo and JD
E-commerce platforms facilitate sales of products while product vendors ...
research
∙
10/08/2018
Entity-Relationship Search over the Web
Entity-Relationship (E-R) Search is a complex case of Entity Search wher...
research
∙
01/23/2018
Entity Retrieval and Text Mining for Online Reputation Monitoring
Online Reputation Monitoring (ORM) is concerned with the use of computat...
research
∙
09/04/2017
Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects
This paper describes a preliminary study for producing and distributing ...
research
∙
04/17/2017
FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings
This paper presents the approach developed at the Faculty of Engineering...
research
∙
10/31/2016
Mining Social Media for Open Innovation in Transportation Systems
This work proposes a novel framework for the development of new products...
research
∙
07/01/2016
