Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search
College of Software, Northeastern University（东北大学软件学院）；Institute of Intelligent Manufacturing, Nanjing Tech University（南京工业大学智能制造研究院）；School of Computer Science, Shaanxi Normal University（陕西师范大学计算机学院）；College of Computer Science, Northeastern University（东北大学计算机科学与工程学院）；Faculty of Technology, Bielefeld University（比勒费尔德大学技术学院）
Lianbo Ma, Nan Li, Guo Yu, Xiaoyu Geng, Min Huang, Xingwei Wang
In the deployment of deep neural models, how to effectively and automatically find feasible deep models under diverse design objectives is fundamental. Most existing neural architecture search (NAS) methods utilize surrogates to predict the detailed performance (e.g., accuracy and model size) of a candidate architecture during the search, which however is complicated and inefficient. In contrast, we aim to learn an efficient Pareto classifier to simplify the search process of NAS by transforming the complex multi-objective NAS task into a simple Pareto-dominance classification task. To this end, we propose a classification-wise Pareto evolution approach for one-shot NAS, where an online classifier is trained to predict the dominance relationship between the candidate and constructed reference architectures, instead of using surrogates to fit the objective functions. The main contribution of this study is to change supernet adaption into a Pareto classifier. Besides, we design two adaptive schemes to select the reference set of architectures for constructing classification boundary and regulate the rate of positive samples over negative ones, respectively. We compare the proposed evolution approach with state-of-the-art approaches on widely-used benchmark datasets, and experimental results indicate that the proposed approach outperforms other approaches and have found a number of neural architectures with different model sizes ranging from 2M to 6M under diverse objectives and constraints.
SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural Architecture Search
LAMIH/UMR CNRS；Université Polytechnique Hauts-de-France（上法兰西理工大学）；University of Lille（里尔大学）；Centre de Recherche en Informatique Signal et Automatique de Lille (CRIStAL)（里尔信息、信号与自动化研究中心（CRIStAL））
Towards Efficient Pareto-optimal Utility-Fairness between Groups in Repeated Rankings
College of Engineering & Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；University of Engineering and Technology, Vietnam National University（越南国家大学工程技术大学）
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education（教育部智能感知与图像理解重点实验室）；International Research Center for Intelligent Perception（智能感知国际研究中心）
AP-BMM: Approximating Capability-Cost Pareto Sets of LLMs via Asynchronous Prior-Guided Bayesian Model Merging
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东新型安全智能技术重点实验室）；Institute of Cyberspace Security（网络空间安全研究院）；School of Computer Science and Technology（计算机科学与技术学院）
