91¸£Àû

Eight papers authored by Computer Science researchers from 91¸£Àû have been accepted for publication at the , the leading international venue for machine learning research, which will be held on 10-15 December 2024 in Vancouver, British Columbia, Canada:

• Generating Origin-Destination Matrices in Neural Spatial Interaction Models, by Ioannis Zachos, Mark Girolami, and Theodoros Damoulas
• Interventionally Consistent Surrogates for Complex Simulation Models, by Joel Dyer, Nicholas Bishop, Yorgos Felekis, Fabio Massimo Zennaro, Ani Calinescu, Theodoros Damoulas, and Michael Wooldridge
• Learning the Expected Core of Strictly Convex Stochastic Cooperative Games, by Phuong Nam Tran, The Anh Ta, Shuqing Shi, Debmalya Mandal, Yali Du, and Long Tran-Thanh
• Physics-Informed Variational State-Space Gaussian Processes, by Oliver Hamelijnck, Arno Solin, and Theodoros Damoulas
• SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series, by Zhihao Dai, Ligang He, Shuanghua Yang, and Matthew Leeke
• Symmetric Linear Bandits with Hidden Symmetry, by Phuong Nam Tran, The Anh Ta, Debmalya Mandal, and Long Tran-Thanh
• The Effectiveness of Surprisingly Popular Voting with Partial Preferences, by Hadi Hosseini, Debmalya Mandal, and Amrit Puhan
• What makes unlearning hard and what to do about it, by Kairan Zhao, Meghdad Kurmanji, George-Octavian Bărbulescu, Eleni Triantafillou, and Peter Triantafillou