Publications

Feedzai Research publishes a series of in-depth articles and research papers on a regular basis. Most of these publications are culminations of fruitful collaboration with leading experts, men and women at world-class research institutions and universities. Our purpose is to keep the research community abreast of the latest in our journey of innovation.

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2023

A Case Study on Designing Evaluations of ML Explanations with Simulated User Studies

Ada Martin, Valerie Chen, Sérgio Jesus, Pedro Saleiro

Published at ICLR 2023 workshop Trustworthy ML

arXiv

Fairness-Aware Data Valuation for Supervised Learning

José Pombal, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro

Published at ICLR 2023 workshop Trustworthy ML

arXiv | Youtube

FairGBM: Gradient Boosting with Fairness Constraints

André F Cruz, Catarina Belém, Sérgio Jesus, João Bravo, Pedro Saleiro, Pedro Bizarro

Published at ICLR 2023, International Conference on Learning Representations

arXiv | Youtube

2022

Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation

Sérgio Jesus, José Pombal, Duarte Alves, André Cruz, Pedro Saleiro, Rita P. Ribeiro, João Gama, Pedro Bizarro

Published at NeurIPS 2022

PDF | arXiv | Kaggle

LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering

Mário Cardoso, Pedro Saleiro, Pedro Bizarro

Published at 3rd ACM International Conference on AI in Finance (ICAIF'22)

PDF | arXiv

Lightweight Automated Feature Monitoring for Data Streams

João Conde, Ricardo Moreira, João Torres, Pedro Cardoso, Hugo Ferreira, Marco O. P. Sampaio, João Tiago Ascensão, Pedro Bizarro

Published at KDD 2022 - AutoML workshop

PDF | arXiv

Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions

José Pombal, André F. Cruz, João Bravo, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro

Published at KDD 2022 - Machine Learning in Finance workshop

PDF | arXiv | Youtube

Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction

José Pombal, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro

Published at ICML 2022 - Responsible Decision Making in Dynamic Environments workshop

PDF | arXiv | Youtube

Human-AI Collaboration in Decision-Making: Beyond Learning to Defer

Diogo Leitão, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro

Published at ICML 2022 - Human-Machine Collaboration and Teaming workshop

PDF | arXiv | Poster | Youtube

A case study on implementing screen reader accessibility in dynamic visualizations

Rita Costa, Beatriz Malveiro, João Palmeiro, Pedro Bizarro

Published at EuroVis 2022 conference

PDF | EuroVis

Data+Shift: Supporting visual investigation of data distribution shifts by data scientists

João Palmeiro, Beatriz Malveiro, Rita Costa, David Polido, Ricardo Moreira, Pedro Bizarro

Published at EuroVis 2022 conference

PDF | EuroVis | ar5iv | Youtube

ConceptDistil: Model-Agnostic Distillation of Concept Explanations

João Bento, Ricardo Moreira, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro

Published at ICLR 2022 - PAIR2Struct workshop

PDF | arXiv

Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs

Ahmad Naser Eddin, Jacopo Bono, David Aparício, David Polido, João Tiago Ascensão, Pedro Bizarro, Pedro Ribeiro

Published at AAAI - AI in Financial Services: Adaptiveness, Resilience & Governance workshop’2022

PDF | arXiv | Youtube

2021

Active learning for imbalanced data under cold start

Ricardo Barata, Miguel Leite, Ricardo Pacheco, Marco O. P. Sampaio, João Tiago Ascensão, Pedro Bizarro

Published at ICAIF 2021 conference

PDF | arXiv | Youtube

GUDIE: A flexible, user-defined method to extract subgraphs of interest from large graphs

Maria Inês Silva, David Aparício, Beatriz Malveiro, João Tiago Ascensão, Pedro Bizarro

Published at ECML PKDD 2021 - Graph Embedding and Mining workshop

PDF | arXiv | Youtube

Finding NeMo: Fishing in banking networks using network motifs

Xavier Fontes, David Aparício, Maria Inês Silva, Beatriz Malveiro, João Tiago Ascensão, Pedro Bizarro

Published at VLDB 2021 - Search, Exploration, and Analysis in Heterogeneous Datastores workshop

PDF | arXiv | Youtube

TimeSHAP: Explaining Recurrent Models through Sequence Perturbations

João Bento, Pedro Saleiro, André Cruz, Mário A. T. Figueiredo, Pedro Bizarro

Published at KDD 2021 conference, NeurIPS 2020 - HAMLETS workshop

PDF | arXiv | Github | Youtube

Active learning for online training in imbalanced data streams under cold start

Ricardo Barata, Miguel Leite, Ricardo Pacheco, Marco O. P. Sampaio, João Tiago Ascensão, Pedro Bizarro

Published at KDD 2021 - Machine Learning in Finance workshop

PDF | Presentation | arXiv | Youtube

GuiltyWalker: Distance to illicit nodes in the Bitcoin network

Catarina Oliveira, João Torres, Maria Inês Silva, David Aparício, João Tiago Ascensão, Pedro Bizarro

Published at KDD 2021 - Machine Learning in Finance workshop

PDF | arXiv | Youtube

Weakly Supervised Multi-task Learning for Concept-based Explainability

Catarina Belém, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro

Published at ICLR 2021 - Weakly Supervised Learning workshop

PDF | arXiv | WeaSuL PDF | Youtube

Promoting Fairness through Hyperparameter Optimization

André Cruz, Pedro Saleiro, Catarina Belém, Carlos Soares, Pedro Bizarro

Published at ICDM 2021 conference, ICLR 2021 - Responsible AI workshop

PDF | arXiv | Github | ICDM PDF | Youtube

How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations

Sérgio Jesus, Catarina Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, João Gama

Published at FAccT 2021 conference

PDF | arXiv | ACM page | Youtube

Railgun: managing large streaming windows under MAD requirements

Ana Sofia Gomes, João Oliveirinha, Pedro Cardoso, Pedro Bizarro

Published at VLDB 2021 conference

PDF | arXiv | VLDB proceedings | Feedzai Techblog | bibtex | Youtube

2020

Teaching the Machine to Explain Itself using Domain Knowledge

Vladimir Balayan, Pedro Saleiro, Catarina Belém, Ludwig Krippahl, Pedro Bizarro

Published at NeurIPS 2020 - HAMLETS workshop

PDF | arXiv | Youtube

A Bandit-Based Algorithm for Fairness-Aware Hyperparameter Optimization

André Cruz, Pedro Saleiro, Catarina Belém, Carlos Soares, Pedro Bizarro

PDF | Github | arXiv

TEDD: Robust Detection of Unstable Temporal Features

Ricardo Pereira, Bruno Casal Laraña, Nádia Soares, Miguel Araújo

Published at IEEE ICDMW 2020 International Conference on Data Mining Workshops

PDF | bibtex | IEEE

Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity

Joana Lorenz, Maria Inês Silva, David Aparício, João Tiago Ascensão, Pedro Bizarro

Published at KDD 2020 - Machine Learning in Finance workshop

PDF | arXiv | Youtube

Interleaved Sequence RNNs for Fraud Detection

Bernardo Branco, Pedro Abreu, Ana Sofia Gomes, Mariana S.C. Almeida, João Tiago Ascensão, Pedro Bizarro

Published at KDD 2020 conference

PDF | arXiv | bibtex | Feedzai Techblog | Youtube

ARMS: Automated rules management system for fraud detection

David Aparicio, Ricardo Barata, João Bravo, João Tiago Ascensão, Pedro Bizarro

PDF | Feedzai Techblog | arXiv

2019

Automatic Model Monitoring for Data Streams

Fábio Pinto, Marco O. P. Sampaio, Pedro Bizarro

Published at KDD 2019 - Anomaly Detection in Finance workshop

PDF | arXiv | bibtex

2017

BreachRadar: Automatic Detection of Points-of-Compromise

Miguel Araújo, Miguel Almeida, Jaime Ferreira, Luís Silva, Pedro Bizarro

Published at SIAM SDM 2017 conference

PDF | arXiv | bibtex

Page printed in 6 Jun 2023. Plase see https://research.feedzai.com/publications for the latest version.