Input Distribution

Input distribution, encompassing the range and probability of different inputs to a machine learning model, is a critical factor influencing model performance and robustness. Current research focuses on adapting models to shifts in input distribution, employing techniques like online density ratio estimation and adaptive algorithms that dynamically adjust to changing data characteristics, as well as analyzing the impact of input distribution on model behavior through methods such as moment propagation and explanation shift analysis. Understanding and mitigating the effects of input distribution shifts is crucial for improving the reliability and generalizability of machine learning models across diverse real-world applications, from aerospace systems to natural language processing.