Uploaded July 2024 | Updated September 2026, 2 weeks ago
Hirokazu Shirado is visiting OIST from 2024-06-20 until 2024-08-19 through the "Theoretical Sciences Visiting Program" (TSVP). Find out more about the TSVP on the program website:
oist.jp/visiting-program.
Abstract: Artificial intelligence is increasingly prevailing in the collective action of human groups, potentially impacting the prosocial norms they have established. In this talk, I present our recent work investigating such a phenomenon through a cyber-physical experiment involving 300 online participants controlling robotic vehicles in a coordination game. Our results indicate that autonomous assistance can hinder the emergence of reciprocity among people, favoring self-interest maximization. Our findings suggest that AI support can fundamentally alter social coordination dynamics, affecting the balance between individual and collective benefits. Finally, I discuss a novel theoretical framework for studying the social dynamics of human-AI hybrids.
Profile: Hirokazu Shirado is an Assistant Professor of the Human-Computer Interaction Institute in the School of Computer Science at Carnegie Mellon University in Pittsburgh, USA. His research focuses on the role of social interactions and technology in the emergence of social order and in the collective confrontation of social dilemmas. He is particularly interested in studying hybrid systems of humans and machines, particularly how artificial intelligence can help people address the challenges of collective action. He has received the NSF CAREER Award and the ASA Outstanding Paper Award for his work.
#OIST #OIST_TSVP #artificialintelligence #cyberphysicalexperiment #hybrids #computerscience Science #research #oist #oist_tsvp #Theoretical #Science #VisitingProgram #Okinawa #TSVP
Hirokazu Shirado is visiting OIST from 2024-06-20 until 2024-08-19 through the "Theoretical Sciences Visiting Program" (TSVP). Find out more about the TSVP on the program website:
oist.jp/visiting-program.
Abstract: Artificial intelligence is increasingly prevailing in the collective action of human groups, potentially impacting the prosocial norms they have established. In this talk, I present our recent work investigating such a phenomenon through a cyber-physical experiment involving 300 online participants controlling robotic vehicles in a coordination game. Our results indicate that autonomous assistance can hinder the emergence of reciprocity among people, favoring self-interest maximization. Our findings suggest that AI support can fundamentally alter social coordination dynamics, affecting the balance between individual and collective benefits. Finally, I discuss a novel theoretical framework for studying the social dynamics of human-AI hybrids.
Profile: Hirokazu Shirado is an Assistant Professor of the Human-Computer Interaction Institute in the School of Computer Science at Carnegie Mellon University in Pittsburgh, USA. His research focuses on the role of social interactions and technology in the emergence of social order and in the collective confrontation of social dilemmas. He is particularly interested in studying hybrid systems of humans and machines, particularly how artificial intelligence can help people address the challenges of collective action. He has received the NSF CAREER Award and the ASA Outstanding Paper Award for his work.
#OIST #OIST_TSVP #artificialintelligence #cyberphysicalexperiment #hybrids #computerscience Science #research #oist #oist_tsvp #Theoretical #Science #VisitingProgram #Okinawa #TSVP

![Florian Yger: A Geometric Adventure in Machine Learning (TSVP Talk at OIST)
[Title]
Florian Yger - A Geometric Adventure in Machine Learning: Learning with Invariances, Structures, and Prior Knowledge (TSVP Talk at OIST)
[Description]
Florian Yger is visiting OIST from 2024-05-20 until 2024-08-15 through the Theoretical Sciences Visiting Program (TSVP). Find out more about the TSVP on the program website:
https://www.oist.jp/visiting-program.
Abstract: In this presentation, we embark on a journey through the landscapes of machine learning, focusing on the role of representation learning. Traditional approaches often restrict themselves to Euclidean spaces, yet many real-world data, such as graphs and covariances, defy this simplistic framework. We explore how embracing non-Euclidean geometries—specifically curved spaces like Riemannian manifolds—unlocks new possibilities in understanding and predicting structured data. This framework allows for the incorporation of prior knowledge such as constraints and invariances into machine learning algorithms.
Central to our exploration is the Fréchet averaging problem, a fundamental tool that generalizes the well-know average to metric spaces. From this cornerstone, we derive many extensions ranging from graph averaging to dimensionality reduction on Riemannian manifolds. We illustrate their practical implications through numerical experiments on biomedical data.
Motivated by challenging applications, this presentation not only highlights the limitations of Euclidean-centric approaches but also underscores the potential of geometry-aware representation learning. Join us as we navigate this geometric adventure into the curved realm of geometry-aware representation learning.
Profile: Florian Yger is an associate professor at Université Paris-Dauphine since 2015, teaching Data Analysis and Machine Learning in the department MIDO. Within the LAMSADE, he is part of the team MILES which focuses on trustworthy Machine Learning and Explainable AI. From 2014 to 2015, he was a JSPS postdoctoral fellow in the laboratory of Prof. Sugiyama at Tokyo University. He received his PhD in Computer science from LITIS, Université de Rouen under the supervision of Alain Rakotomamonjy in 2013. He contributes to the problem of representation learning with a particular interest in the representation of structured data (graphs, covariance matrices,…) and the development of learning algorithm for non-Euclidean spaces. This work has many applications ranging from signal processing (EEG signals and Brain Computer Interface) and to image processing (Paintings for art style recognition). More recently, within MILES teams, he addresses the questions of trust, explainability and interpretability in machine learning models with a focus counterfactual reasoning on data. He recently started studying the interplay between computational social choice and machine learning in the context of voter’s opinion aggregation. He is a visiting researcher at RIKEN AIP, Japan since 2017, is a member of the Prairie (PaRis Artificial Intelligence Research InstitutE) where he holds a junior chair, and an Affiliate of the Theoretical Sciences Visiting Program (TSVP) at OIST.
#OIST #OIST_TSVP #MachineLearning #Geometry #Manifolds #Theoretical #Science #VisitingProgram #Okinawa #TSVP Florian Yger: A Geometric Adventure in Machine Learning (TSVP Talk at OIST)](https://i.ytimg.com/vi/KlU1mS1xr3I/mqdefault.jpg)








