Question Based Dependency

Question-based dependency research explores how relationships between data points, whether in tabular data, graphs, time series, or natural language, influence analysis and prediction. Current research focuses on developing methods to effectively model and utilize these dependencies, employing techniques like transformers, graph convolutional networks, and optimal transport to capture complex interactions across various data modalities. This work is crucial for improving the accuracy and reliability of machine learning models across diverse applications, including synthetic data generation, causal inference, and question answering systems. Understanding and leveraging these dependencies is key to building more robust and insightful AI systems.