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
Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration
Risto Trajanov, Ana Nikolikj, Gjorgjina Cenikj, Fabien Teytaud, Mathurin Videau, Olivier Teytaud, Tome Eftimov, Manuel López-Ibáñez, Carola Doerr
SC-Square: Future Progress with Machine Learning?
Matthew England
Multi-Agent Path Finding on Strongly Connected Digraphs: feasibility and solution algorithms
Stefano Ardizzoni, Irene Saccani, Luca Consolini, Marco Locatelli
Multitask Learning via Shared Features: Algorithms and Hardness
Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan Ullman, Lydia Zakynthinou
Biblio-Analysis of Cohort Intelligence (CI) Algorithm and its allied applications from Scopus and Web of Science Perspective
Ishaan Kale, Rahul Joshi, Kalyani Kadam