中心活动

2021年

当前位置: 首页 > 中心活动 > 年度主题活动 > 2021年 > 正文

Statistical learning methods in modern AI

日期：2021-06-17 点击：

1. Conference information

Statistics is one of the key foundations of Artificial Intelligence (AI). In recent years, with the development of modern technologies, AI-related research has evolved into a multiple interdisciplinary field, covering computer science, statistical/machine learning, psychology, neuroscience, materials science, mechanical engineering, and computer hardware design, among others. Statistics as a discipline is facing unprecedented challenges as well as opportunities. There are two major issues that statisticians need to seriously consider. The first one is how to use statistical thinking and methods to improve and innovate artificial intelligence algorithms, and further explore the possible statistical directions in the field of artificial intelligence. The second one is how to use artificial intelligence to improve and innovate statistical thinking and methods, and further develop statistical theory suitable for modern AI. This meeting sponsored by the Tianyuan Mathematical Center in Northwest China will invite data scientists, statisticians and young scholars both in China and abroad. Focusing on the following four topics, the conference will have in-depth discussions on the statistical foundations of modern artificial intelligence, aiming to provide some cutting-edge statistical ideas and methods for AI-related algorithms. Topics include but are not limited to:

- Statistical learning theory for unsupervised, semi-supervised, and supervised representation learning and reinforcement learning

- Deep Learning (architectures, generative models, deep reinforcement learning, etc.)

- Probabilistic Inference (Bayesian methods, graphical models, Monte Carlo methods, etc.)

- Applications in audio, speech, ecommerce, robotics, crowdsourcing, healthcare, neuroscience, neuroscience, computational biology, or any other field

- Statistical optimization for learning and AI.

2. Organizer

The Tianyuan Mathematical Center in Northwest China (hereafter: “the center”) is a mathematical research institute (platform) established by the Tianyuan Mathematical Foundation of the National Natural Science Foundation of China, with the purpose of promoting mathematics study in China to an advanced global level, and formulating a balanced development of mathematics in both geographic regions and academic fields in China.

The positioning of the Center: The center bases in the Northwest China, serves the nation and aims to carry out international leading research. The center aims at building a frontier research base for cross-disciplinary research on mathematics and other disciplines, a place able to undertake national significant tasks, a research and development base and talent magnet of mathematics and mathematical technology, and a training base for innovative people of applied mathematics.

The main tasks of the Center: Conducting academic exchanges facing the frontiers of disciplines, organizing major national needs discussion of major cross-cutting issues and research on major topics. Implement the "Tianyuan scholar/postdoctoral" project to promote mathematics regional balance between research and talent training. Plan and hold "National Summer School of Applied Mathematics" and "National Summer School for College Mathematics Teachers", which is to promote the development of applied mathematics and the cultivation of mathematics teachers in central and western China.

In 2021, the Center will organize a series of high-level academic activities focusing on “Optimization Methods and Artificial Intelligence”, the conferences titled " Statistical learning methods in modern AI" is one of the most important activities. The well-known experts、scholars and graduate students in this fields are invited to exchange and discuss. The 2021 “Optimization Methods and Artificial Intelligence” theme year activity is expected to develop more effective artificial intelligence methods, and give birth to new branches of modern Statistical learning, so as to contribute to the development of national intelligent industry.