会议预告｜稳定学习:找出因果推理和机器学习之间的共同点

ACM CIKM19 and MMM2020. He is a Distinguished Member of ACM and CCF, and Senior Member of IEEE.

分析报告段落

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

稳定努力学习:找本质侦探小说和机器努力学习之间的共通点

在一个常见于的机器努力学习问题里面，采用训练原始数据集估计的基本概念，根据判读到的特点预报期望的结果值。当测试原始数据和训练原始数据来自相同的常见于时，许多努力学习搜索算法被提出异议并被证明是成功的。然而，对于集合的训练原始数据常见于，性能最好的基本概念通常会利用特点之间的微妙统计人关系，这使得它们在运用测试常见于与训练原始数据不同的原始数据时，可能较易出现预报偏差。如何开发对原始数据移出具有稳定性和鲁棒性的努力学习基本概念，对于学术研究者和实际应用领域都至关重要。本质侦探小说是指根据本质人关系发生的前提得出结论的步骤，是解释性和稳定性努力学习的一种强大的统计建模工具。在本次演讲里面，我们将定位于稳定努力学习的最新进展，旨在从观测原始数据里面探险本质知识，以提高机器努力学习搜索算法的可解释性和稳定性。

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.

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