Accepted Papers
This page is archived from the 2023 event. Please see the home page for up-to-date information
Accepted vision papers are compiled in the Proceedings of Machine Learning Research Volume 208.
All accepted papers have further been presented as a poster at the Continual Causality Bridge. In addition, four papers have been selected for additional contributed talks.
If you have missed the inaugural AAAI-23 Continual Causality Bridge, we, the organizers, have also written a brief article to summarize the event, recap trends and highlight the many great discussions.
A Retrospective of the Inaugural AAAI-23 Bridge Program
- Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program
Martin Mundt, Keiland W. Cooper, Devendra Singh Dhami, Adèle Ribeiro, James Seale Smith, Alexis Bellot, Tyler Hayes
Selected for Oral Presentation
- Modeling Uplift from Observational Time-Series in Continual Scenarios
Sanghyun Kim, Jungwon Choi, NamHee Kim, Jaesung Ryu, Juho Lee - From IID to the Independent Mechanisms assumption in continual learning
Oleksiy Ostapenko, Pau Rodríguez, Alexandre Lacoste, Laurent Charlin - Never Ending Reasoning and Learning: Opportunities and Challenges
Sriraam Natarajan, Kristian Kersting - Towards Causal Replay for Knowledge Rehearsal in Continual Learning
Nikhil Churamani, Jiaee Cheong, Sinan Kalkan, Hatice Gunes
Selected for Poster Presentation
- Issues for Continual Learning in the Presence of Dataset Bias
Donggyu Lee, Sangwon Jung, Taesup Moon - From Continual Learning to Causal Discovery in Robotics
Luca Castri, Sariah Mghames, Nicola Bellotto - Spurious Features in Continual Learning
Timothée Lesort - Causal Concept Identification in Open World Environments
Moritz Willig, Matej Zečević, Jonas Seng, Florian Peter Busch - Continual Causal Abstractions
Matej Zečević, Moritz Willig, Florian Peter Busch, Jonas Seng - Treatment Effect Estimation to Guide Model Optimization in Continual Learning
Jonas Seng, Florian Peter Busch, Matej Zečević, Moritz Willig - Continually Updating Neural Causal Models
Florian Peter Busch, Jonas Seng, Moritz Willig, Matej Zečević - Prospects of Continual Causality for Industrial Applications
Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu - Continual Treatment Effect Estimation: Challenges and Opportunities
Zhixuan Chu, Sheng Li