中国计算机学会推荐国际学术会议和期刊目录自2010年8月首次发布以来，已历经五版，得到了计算机领域科研工作者的广泛关注。

目录共包含十个领域，分为ABC三类：A类是国际上极少数顶会与顶刊；B类代表领域内高水平的会议与期刊；C类指国际上重要的、为学术界所认可的优秀会议和期刊。

SAGT 2024

The 17th International Symposium on Algorithmic Game Theory (SAGT) will be held at Centrum Wiskunde & Informatica (CWI) in Amsterdam, The Netherlands, September 3–6, 2024.

The purpose of SAGT is to bring together researchers from Computer Science, Economics, Mathematics, Operations Research, Psychology, Physics, and Biology to present and discuss original research at the intersection of Algorithms and Game Theory.

所属领域：交叉/综合/新兴

CCF分级：C类

时间地点：2024年9月3日-阿姆斯特丹（荷兰）

截稿时间：2024年5月14日

大会征文

Solution Concepts in Game Theory

Efficiency of Equilibria and Price of Anarchy

Computational Aspects of Equilibria

Learning and Dynamics in Games

Game-Theoretic Aspects of Networks

Auction Design and Analysis

Algorithmic Contract Design

Mechanism Design and Pricing

Internet Economics and Computational Advertising

Reputation, Recommendation and Trust Systems

Economic Aspects of Distributed Computing

Blockchain and Cryptocurrencies

Decision Theory and Information Design

Computational Social Choice and Fair Division

Market Design and Matching Markets

Cooperative Game Theory

NeurIPS 2024

The conference was founded in 1987 and is now a multi-track interdisciplinary annual meeting that includes invited talks, demonstrations, symposia, and oral and poster presentations of refereed papers. Along with the conference is a professional exposition focusing on machine learning in practice, a series of tutorials, and topical workshops that provide a less formal setting for the exchange of ideas.

所属领域：人工智能

CCF分级：A类

时间地点：2024年12月9日-温哥华（加拿大）

截稿时间：2024年5月15日

大会征文

Applications (e.g., vision, language, speech and audio, Creative AI)

Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)

Evaluation (e.g., methodology, meta studies, replicability and validity, human-in-the-loop)

General machine learning (supervised, unsupervised, online, active, etc.)

Infrastructure (e.g., libraries, improved implementation and scalability, distributed solutions)

Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences)

Neuroscience and cognitive science (e.g., neural coding, brain-computer interfaces)

Optimization (e.g., convex and non-convex, stochastic, robust)

Probabilistic methods (e.g., variational inference, causal inference, Gaussian processes)

Reinforcement learning (e.g., decision and control, planning, hierarchical RL, robotics)

Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Theory (e.g., control theory, learning theory, algorithmic game theory)