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Found 6 papers, 5 papers with code
Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs
The goal is to minimize the need for costly domain-specific data for subsequent fine-tuning while achieving desired performance levels.
2D-Shapley: A Framework for Fragmented Data Valuation
Data valuation -- quantifying the contribution of individual data sources to certain predictive behaviors of a model -- is of great importance to enhancing the transparency of machine learning and designing incentive systems for data sharing.
LAVA: Data Valuation without Pre-Specified Learning Algorithms
(1) We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between training and validation sets.
Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information
With poisoning equal to or less than 0. 5% of the target-class data and 0. 05% of the training set, we can train a model to classify test examples from arbitrary classes into the target class when the examples are patched with a backdoor trigger.
Ranked #1 on
Clean-label Backdoor Attack (0.05%)
on Tiny ImageNet
Label-Only Model Inversion Attacks via Boundary Repulsion
In this paper, we introduce an algorithm, Boundary-Repelling Model Inversion (BREP-MI), to invert private training data using only the target model's predicted labels.
ModelPred: A Framework for Predicting Trained Model from Training Data
In this work, we propose ModelPred, a framework that helps to understand the impact of changes in training data on a trained model.
