no code implementations • 6 Feb 2024 • Patrick Altmeyer, Andrew M. Demetriou, Antony Bartlett, Cynthia C. S. Liem
Humans have a tendency to see 'human'-like qualities in objects around them.
1 code implementation • 17 Dec 2023 • Patrick Altmeyer, Mojtaba Farmanbar, Arie van Deursen, Cynthia C. S. Liem
We formalise this notion of faithfulness through the introduction of a tailored evaluation metric and propose a novel algorithmic framework for generating Energy-Constrained Conformal Counterfactuals that are only as plausible as the model permits.
1 code implementation • 16 Aug 2023 • Patrick Altmeyer, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, Cynthia C. S. Liem
Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment: given some estimated model, the goal is to find valid counterfactuals for an individual instance that fulfill various desiderata.
1 code implementation • 14 Aug 2023 • Patrick Altmeyer, Arie van Deursen, Cynthia C. S. Liem
We present CounterfactualExplanations. jl: a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box models in Julia.
2 code implementations • 1 Sep 2022 • Savvina Daniil, Mirjam Cuper, Cynthia C. S. Liem, Jacco van Ossenbruggen, Laura Hollink
We find that popular books are mainly written by US citizens in the dataset, and that these books tend to be recommended disproportionally by popular collaborative filtering algorithms compared to the users' profiles.
no code implementations • 10 May 2022 • Han-Yin Huang, Cynthia C. S. Liem
Artificial intelligence literature suggests that minority and fragile communities in society can be negatively impacted by machine learning algorithms due to inherent biases in the design process, which lead to socially exclusive decisions and policies.
no code implementations • 15 Dec 2020 • Cynthia C. S. Liem, Annibale Panichella
To understand whether and how researchers in SE address these threats, we surveyed 45 recent papers related to three predictive tasks: defect prediction (DP), predictive mutation testing (PMT), and code smell detection (CSD).
Software Engineering
1 code implementation • 1 Dec 2020 • Burak Yildiz, Hayley Hung, Jesse H. Krijthe, Cynthia C. S. Liem, Marco Loog, Gosia Migut, Frans Oliehoek, Annibale Panichella, Przemyslaw Pawelczak, Stjepan Picek, Mathijs de Weerdt, Jan van Gemert
We present ReproducedPapers. org: an open online repository for teaching and structuring machine learning reproducibility.
no code implementations • 15 Apr 2019 • Jaehun Kim, Julián Urbano, Cynthia C. S. Liem, Alan Hanjalic
The underlying assumption is that in case a deep representation is to be trusted, distance consistency between known related points should be maintained both in the input audio space and corresponding latent deep space.
1 code implementation • 5 May 2018 • Jaehun Kim, Minz Won, Xavier Serra, Cynthia C. S. Liem
The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy.
1 code implementation • 12 Feb 2018 • Jaehun Kim, Julián Urbano, Cynthia C. S. Liem, Alan Hanjalic
In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain.
no code implementations • 2 Feb 2018 • Hugo Jair Escalante, Heysem Kaya, Albert Ali Salah, Sergio Escalera, Yagmur Gucluturk, Umut Guclu, Xavier Baro, Isabelle Guyon, Julio Jacques Junior, Meysam Madadi, Stephane Ayache, Evelyne Viegas, Furkan Gurpinar, Achmadnoer Sukma Wicaksana, Cynthia C. S. Liem, Marcel A. J. van Gerven, Rob van Lier
Explainability and interpretability are two critical aspects of decision support systems.