Influence Functions for Data Attribution in Linear System Identification and LQR Control
Understanding the influence of individual training data points is crucial for developing reliable machine learning-based control systems. However, conventional methods like leave-one-out retraining are computationally infeasible for large datasets. This paper introduces a framework using influence functions to efficiently approximate the impact of removing specific training trajectories on both learned system dynamics and downstream control performance. We formulate two influence functions(IF): IF1, which estimates the effect on the predictive accuracy of a learned linear dynamics model, and IF2, which quantifies the subsequent impact on the cost of a Linear Quadratic Regulator (LQR) controller designed using these learned dynamics. These involve tracing sensitivities through the Discrete Algebraic Riccati Equation (DARE) solution. We empirically validate our approach on simulated linear systems analogous to robotic manipulators. Results show strong positive correlations between influence predictions and ground truth changes obtained via retraining. Our framework provides a computationally tractable method for data attribution.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Influence Functions for Scalable Data Attribution in Diffusion Models
Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In this paper, we aim to help address such ch…
Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution
As large language models are increasingly trained and fine-tuned, practitioners need methods to identify which training data drive specific behaviors, particularly unintended ones. Training Data Attribution (TDA) methods…
Semantic SimilarityInfluence-based Attributions can be Manipulated
Influence Functions are a standard tool for attributing predictions to training data in a principled manner and are widely used in applications such as data valuation and fairness. In this work, we present realistic ince…
Data ValuationFairnessRevisiting Data Attribution for Influence Functions
The goal of data attribution is to trace the model's predictions through the learning algorithm and back to its training data. thereby identifying the most influential training samples and understanding how the model's b…
Better Hessians Matter: Studying the Impact of Curvature Approximations in Influence Functions
Influence functions offer a principled way to trace model predictions back to training data, but their use in deep learning is hampered by the need to invert a large, ill-conditioned Hessian matrix. Approximations such a…