会议预告｜稳定学习:寻找因果推理和人工神经网络之间的共同点

KM19 and MMM2020. He is a Distinguished Member of ACM and CCF, and Senior Member of IEEE.

调查结果内容

Stable Learning: Finding the Common Ground between Causal Inference and Machine Learning

平稳深造:寻找任何事物废话和机器深造错综复杂的相异

在一个常见的机器深造问题中，使用特训数据集估计的框架，根据观察到的构造分析更进一步的结果值。当测试数据和特训数据来自并不相同的分布时，许多深造搜索算法被提出并被证明是取得成功的。然而，对于任意的特训数据分布，性能最好的框架通常时会利用构造错综复杂的微妙统计关系，这使得它们在应用领域于测试分布与特训数据不同的数据时，可能更容易出现分析错误。如何合作开发对数据转移具有准确度和鲁棒性的深造框架，对于研究成果和实际应用领域都至关重要。任何事物废话是所称根据任何事物关系发生的条件确实的过程，是解释性和准确度深造的一种强大的统计建模工具。在本次致辞中，我们将聚焦于平稳深造的最新进展，旨在从观测数据中探求任何事物知识，以提高机器深造搜索算法的可解释性和准确度。

Predicting future outcome values based on their observed features using a model estimated on a training data set in a common machine learning problem. Many learning algorithms have been proposed and shown to be successful when the test data and training data come from the same distribution. However, the best-performing models for a given distribution of training data typically exploit subtle statistical relationships among features, making them potentially more prone to prediction error when applied to test data whose distribution differs from that in training data. How to develop learning models that are stable and robust to shifts in data is of paramount importance for both academic research and real applications. Causal inference, which refers to the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect, is a powerful statistical modeling tool for explanatory and stable learning. In this talk, we focus on the latest progress of stable learning, aiming to explore causal knowledge from observational data to improve the interpretability and stability of machine learning algorithms.

👇 点个“在看”分享不单是

。

镇江看妇科哪里比较好
西宁白癜风医院哪家好
昆明皮肤病检查费用
急支糖浆适合哪种咳嗽
性病用药
止咳糖浆怎么选
心跳过速
泻下药