Publications

Roboethics for everyone – A hands-on teaching module for K-12 and beyond
Rahatul Amin Ananto
Shalaleh Rismani
Lixiao Zhu
Christopher Yee Wong
AJung Moon
In this work, we address the evolving landscape of roboethics, expanding beyond physical safety to encompass broader societal implications. … (voir plus)Recognizing the siloed nature of existing initiatives to teach and inform ethical implications of artificial intelligence (AI) and robotic systems, we present a roboethics teaching module designed for K-12 students and general audiences. The module focuses on the high-level analysis of the interplay between robot behaviour design choices and ethics, using everyday social dilemmas. We delivered the module in a workshop to high school students in Montreal, Canada. From this experience, we observed that the module successfully fostered critical thinking and ethical considerations in students, without requiring advanced technical knowledge. This teaching module holds promise to reach a wider range of populations. We urge the education community to explore similar approaches and engage in interdisciplinary training opportunities regarding the ethical implications of AI and robotics.
2024-12-20
Proceedings of the Canadian Engineering Education Association (CEEA) (publié)
doi.org
Roboethics for everyone – A hands-on teaching module for K-12 and beyond
Rahatul Amin Ananto
Shalaleh Rismani
Lixiao Zhu
Christopher Yee Wong
AJung Moon
In this work, we address the evolving landscape of roboethics, expanding beyond physical safety to encompass broader societal implications. … (voir plus)Recognizing the siloed nature of existing initiatives to teach and inform ethical implications of artificial intelligence (AI) and robotic systems, we present a roboethics teaching module designed for K-12 students and general audiences. The module focuses on the high-level analysis of the interplay between robot behaviour design choices and ethics, using everyday social dilemmas. We delivered the module in a workshop to high school students in Montreal, Canada. From this experience, we observed that the module successfully fostered critical thinking and ethical considerations in students, without requiring advanced technical knowledge. This teaching module holds promise to reach a wider range of populations. We urge the education community to explore similar approaches and engage in interdisciplinary training opportunities regarding the ethical implications of AI and robotics.
2024-12-20
Proceedings of the Canadian Engineering Education Association (CEEA) (publié)
doi.org
Robust Guided Diffusion for Offline Black-Box Optimization
Can Chen
Christopher Beckham
Zixuan Liu
Xue (Steve) Liu
Chris Pal
Offline black-box optimization aims to maximize a black-box function using an offline dataset of designs and their measured properties. Two … (voir plus)main approaches have emerged: the forward approach, which learns a mapping from input to its value, thereby acting as a proxy to guide optimization, and the inverse approach, which learns a mapping from value to input for conditional generation. (a) Although proxy-free~(classifier-free) diffusion shows promise in robustly modeling the inverse mapping, it lacks explicit guidance from proxies, essential for generating high-performance samples beyond the training distribution. Therefore, we propose \textit{proxy-enhanced sampling} which utilizes the explicit guidance from a trained proxy to bolster proxy-free diffusion with enhanced sampling control. (b) Yet, the trained proxy is susceptible to out-of-distribution issues. To address this, we devise the module \textit{diffusion-based proxy refinement}, which seamlessly integrates insights from proxy-free diffusion back into the proxy for refinement. To sum up, we propose \textit{\textbf{R}obust \textbf{G}uided \textbf{D}iffusion for Offline Black-box Optimization}~(\textbf{RGD}), combining the advantages of proxy~(explicit guidance) and proxy-free diffusion~(robustness) for effective conditional generation. RGD achieves state-of-the-art results on various design-bench tasks, underscoring its efficacy. Our code is at https://anonymous.4open.science/r/RGD-27A5/README.md.
2024-12-20
TMLR (accepté)
doi.org
openreview.net
Editorial: Special Issue on Software Engineering and AI for Data Quality
Foutse Khomh
Andreas Metzger
Phu Nguyen
Sagar Sen
This editorial summarizes the content of the Special Issue on Software Engineering and AI for Data Quality of the Journal of Data and Inform… (voir plus)ation Quality (JDIQ).
2024-12-19
Journal of Data and Information Quality (publié)
doi.org
Editorial: Special Issue on Software Engineering and AI for Data Quality
Foutse Khomh
Andreas Metzger
Phu Nguyen
Sagar Sen
This editorial summarizes the content of the Special Issue on Software Engineering and AI for Data Quality of the Journal of Data and Inform… (voir plus)ation Quality (JDIQ).
2024-12-19
Journal of Data and Information Quality (publié)
doi.org
Editorial: Special Issue on Software Engineering and AI for Data Quality
Foutse Khomh
Andreas Metzger
Phu H. Nguyen
Sagar Sen
This editorial summarizes the content of the Special Issue on Software Engineering and AI for Data Quality of the Journal of Data and Inform… (voir plus)ation Quality (JDIQ).
2024-12-19
Journal of Data and Information Quality (publié)
doi.org
Scaling 4D Representations
João Carreira
Dilara Gokay
Michael King
Chuhan Zhang
Ignacio Rocco
Aravindh Mahendran
T. Keck
Joseph Heyward
Skanda Koppula
Etienne Pot
Goker Erdogan
Yana Hasson
Yi Yang
Klaus Greff
Guillaume Le Moing
Sjoerd van Steenkiste
Daniel Zoran
Drew A. Hudson
Pedro V'elez
Luisa F. Polan'ia … (voir 15 de plus)
Luke Friedman
Chris Duvarney
Ross Goroshin
Kelsey Allen
Jacob Walker
Rishabh Kabra
Eric Aboussouan
Jennifer Sun
Thomas Kipf
Carl Doersch
Viorica Puatruaucean
Dima Damen
Pauline Luc
Mehdi S. M. Sajjadi
Andrew Zisserman
Scaling has not yet been convincingly demonstrated for pure self-supervised learning from video. However, prior work has focused evaluations… (voir plus) on semantic-related tasks
2024-12-19
ArXiv (prépublication)
doi.org
arxiv.org
Delays in Care for Children With Low Anorectal Malformations in Southwestern Uganda.
Felix Oyania
Caroline Q. Stephens
Sarah Ullrich
Meera Kotagal
Daniel Kisitu
Francis Bajunirwe
Doruk Ozgediz
Dan Poenaru
2024-12-18
Journal of Surgical Research (publié)
doi.org
Delays in Care for Children With Low Anorectal Malformations in Southwestern Uganda.
Felix Oyania
Caroline Q. Stephens
Sarah Ullrich
Meera Kotagal
Daniel Kisitu
Francis Bajunirwe
Doruk Ozgediz
Dan Poenaru
2024-12-18
Journal of Surgical Research (publié)
doi.org
Delays in Care for Children With Low Anorectal Malformations in Southwestern Uganda.
Felix Oyania
Caroline Q. Stephens
Sarah Ullrich
Meera Kotagal
Daniel Kisitu
Francis Bajunirwe
Doruk Ozgediz
Dan Poenaru
2024-12-18
Journal of Surgical Research (publié)
doi.org
Embedding Cultural Diversity in Prototype-based Recommender Systems
Armin Moradi
Nicola Neophytou
Florian Carichon
Golnoosh Farnadi
Popularity bias in recommender systems can increase cultural overrepresentation by favoring norms from dominant cultures and marginalizing u… (voir plus)nderrepresented groups. This issue is critical for platforms offering cultural products, as they influence consumption patterns and human perceptions. In this work, we address popularity bias by identifying demographic biases within prototype-based matrix factorization methods. Using the country of origin as a proxy for cultural identity, we link this demographic attribute to popularity bias by refining the embedding space learning process. First, we propose filtering out irrelevant prototypes to improve representativity. Second, we introduce a regularization technique to enforce a uniform distribution of prototypes within the embedding space. Across four datasets, our results demonstrate a 27\% reduction in the average rank of long-tail items and a 2\% reduction in the average rank of items from underrepresented countries. Additionally, our model achieves a 2\% improvement in HitRatio@10 compared to the state-of-the-art, highlighting that fairness is enhanced without compromising recommendation quality. Moreover, the distribution of prototypes leads to more inclusive explanations by better aligning items with diverse prototypes.
2024-12-18
ArXiv (prépublication)
doi.org
arxiv.org
Embedding Cultural Diversity in Prototype-based Recommender Systems
Armin Moradi
Nicola Neophytou
Florian Carichon
Golnoosh Farnadi
Popularity bias in recommender systems can increase cultural overrepresentation by favoring norms from dominant cultures and marginalizing u… (voir plus)nderrepresented groups. This issue is critical for platforms offering cultural products, as they influence consumption patterns and human perceptions. In this work, we address popularity bias by identifying demographic biases within prototype-based matrix factorization methods. Using the country of origin as a proxy for cultural identity, we link this demographic attribute to popularity bias by refining the embedding space learning process. First, we propose filtering out irrelevant prototypes to improve representativity. Second, we introduce a regularization technique to enforce a uniform distribution of prototypes within the embedding space. Across four datasets, our results demonstrate a 27\% reduction in the average rank of long-tail items and a 2\% reduction in the average rank of items from underrepresented countries. Additionally, our model achieves a 2\% improvement in HitRatio@10 compared to the state-of-the-art, highlighting that fairness is enhanced without compromising recommendation quality. Moreover, the distribution of prototypes leads to more inclusive explanations by better aligning items with diverse prototypes.
2024-12-18
ArXiv (prépublication)
arxiv.org
arxiv.org