Department of Mathematics and Statistics, University of Regina（Regina 大学数学与统计学系）；Department of Computer Science, University of Regina（Regina 大学计算机科学系）；Department of Computer Science, University of British Columbia（不列颠哥伦比亚大学计算机科学系）；Department of Computer Science, Simon Fraser University（Simon Fraser 大学计算机科学系）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔人工智能研究所）
Shaun Fallat, Kamyar Khodamoradi, David Kirkpatrick, Valerii Maliuk, S. Ahmad Mojallal, Sandra Zilles
We study the problem of learning hypergraphs with shortest-path queries (SP-queries), and present the first provably optimal online algorithm for a broad and natural class of hypertrees that we call orderly hypertrees. Our online algorithm can be transformed into a provably optimal offline algorithm. Orderly hypertrees can be positioned within the Fagin hierarchy of acyclic hypergraph (well-studied in database theory), and strictly encompass the broadest class in this hierarchy that is learnable with subquadratic SP-query complexity.
Recognizing that in some contexts, such as evolutionary tree reconstruction, distance measurements can degrade with increased distance, we also consider a learning model that uses bounded distance queries. In this model, we demonstrate asymptotically tight complexity bounds for learning general hypertrees.
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data
Stanford University（斯坦福大学）；Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai University of Finance and Economics（上海财经大学）
Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning
Vrije Universiteit Brussel（布鲁塞尔自由大学）；Université de Montréal（蒙特利尔大学）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；University of Galway（戈尔韦大学）；City of Amsterdam（阿姆斯特丹市）；Utrecht University（乌得勒支大学）
MGDA Converges under Generalized Smoothness, Provably
School of Electrical, Computer and Energy Engineering, Arizona State University（亚利桑那州立大学电气、计算机与能源工程学院）；Department of Computer Science and Engineering, University at Buffalo（纽约州立大学布法罗分校计算机科学与工程学院）
What resources are needed for optimal learning of bosonic Gaussian states?
Institute for Quantum Information and Matter（量子信息研究所）；Caltech（加州理工学院）；Scuola Normale Superiore（高等师范大学）；Inria（法国国家科学与技术研究院）；Télécom Paris – LTCI（巴黎电信学院 – LTCI）；Institut Polytechnique de Paris（巴黎理工学院）
