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Found 2 papers, 1 papers with code
DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting
To address this challenge, we propose DoubleAdapt, an end-to-end framework with two adapters, which can effectively adapt the data and the model to mitigate the effects of distribution shifts.
Resolving Training Biases via Influence-based Data Relabeling
To achieve this, we use influence functions to estimate how relabeling a training sample would affect model's test performance and further develop a novel relabeling function R. We theoretically prove that applying R to relabel harmful training samples allows the model to achieve lower test loss than simply discarding them for any classification tasks using cross-entropy loss.
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