Papers Influence Approximation
“Influence Approximation” 태그가 달린 논문 7편 · 필터 해제
ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs
Gradient-based data influence approximation has been leveraged to select useful data samples in the supervised fine-tuning of large language models. However, the computation of gradients throughout the fine-tuning proces…
ClusteringInfluence ApproximationLazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data
In Federated Learning, heterogeneity in client data distributions often means that a single global model does not have the best performance for individual clients. Consider for example training a next-word prediction mod…
Federated LearningInfluence ApproximationPersonalized Federated LearningDeeper Understanding of Black-box Predictions via Generalized Influence Functions
Influence functions (IFs) elucidate how training data changes model behavior. However, the increasing size and non-convexity in large-scale models make IFs inaccurate. We suspect that the fragility comes from the first-o…
Influence ApproximationPhilosophyDataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Quantifying the impact of training data points is crucial for understanding the outputs of machine learning models and for improving the transparency of the AI pipeline. The influence function is a principled and popular…
Influence Approximationparameter-efficient fine-tuningLIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data. However, traditional data valuation methods are not suitable due to privacy concerns. To address this, we propose a simple yet eff…
Data ValuationFederated LearningInfluence ApproximationPrivacy PreservingExplaining Neural Matrix Factorization with Gradient Rollback
Explaining the predictions of neural black-box models is an important problem, especially when such models are used in applications where user trust is crucial. Estimating the influence of training examples on a learned …
Graph EmbeddingInfluence ApproximationKnowledge Base CompletionKnowledge Graph EmbeddingOn the Accuracy of Influence Functions for Measuring Group Effects
Influence functions estimate the effect of removing a training point on a model without the need to retrain. They are based on a first-order Taylor approximation that is guaranteed to be accurate for sufficiently small c…
Influence Approximation