Practical Algorithm
Practical algorithm research focuses on developing and improving algorithms for diverse applications, prioritizing efficiency, accuracy, and interpretability. Current research emphasizes areas like efficient model training and inference (e.g., low-bit quantization for LLMs, distributed algorithms for large datasets), robust optimization techniques (e.g., evolutionary algorithms, Q-learning variants), and methods for handling noisy data or dynamic environments. These advancements have significant implications across various fields, including machine learning, robotics, and data analysis, by enabling more efficient and reliable solutions to complex problems.
Papers
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms
Pranay Sharma, Rohan Panda, Gauri Joshi, Pramod K. Varshney
Representation, learning, and planning algorithms for geometric task and motion planning
Beomjoon Kim, Luke Shimanuki, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Deciding Cuspidality of Manipulators through Computer Algebra and Algorithms in Real Algebraic Geometry
Damien Chablat, Rémi Prébet, Mohab Safey El Din, Durgesh Salunkhe, Philippe Wenger
Video Question Answering: Datasets, Algorithms and Challenges
Yaoyao Zhong, Junbin Xiao, Wei Ji, Yicong Li, Weihong Deng, Tat-Seng Chua
A density peaks clustering algorithm with sparse search and K-d tree
Yunxiao Shan, Shu Li, Fuxiang Li, Yuxin Cui, Shuai Li, Ming Zhou, Xiang Li
KC-TSS: An Algorithm for Heterogeneous Robot Teams Performing Resilient Target Search
Minkyu Kim, Ryan Gupta, Luis Sentis