IM-Context: In-Context Learning for Imbalanced Regression Tasks (2405.18202v2)

Abstract: Regression models often fail to generalize effectively in regions characterized by highly imbalanced label distributions. Previous methods for deep imbalanced regression rely on gradient-based weight updates, which tend to overfit in underrepresented regions. This paper proposes a paradigm shift towards in-context learning as an effective alternative to conventional in-weight learning methods, particularly for addressing imbalanced regression. In-context learning refers to the ability of a model to condition itself, given a prompt sequence composed of in-context samples (input-label pairs) alongside a new query input to generate predictions, without requiring any parameter updates. In this paper, we study the impact of the prompt sequence on the model performance from both theoretical and empirical perspectives. We emphasize the importance of localized context in reducing bias within regions of high imbalance. Empirical evaluations across a variety of real-world datasets demonstrate that in-context learning substantially outperforms existing in-weight learning methods in scenarios with high levels of imbalance.

• The paper presents IM-Context, an innovative method that uses localized in-context learning to mitigate bias in imbalanced regression.
• It demonstrates that selecting only the closest context samples significantly improves accuracy, especially in few-shot and sparse regions.
• Empirical results on datasets like AgeDB-DIR and STS-B-DIR confirm that IM-Context consistently outperforms traditional in-weight learning approaches.

In-Context Learning for Imbalanced Regression Tasks

Introduction

The challenges of imbalanced regression, distinct from imbalanced classification, are increasingly relevant in various applications like age estimation in computer vision and engineering design. Where traditional regression models often falter due to their bias toward majority labels and a tendency to overfit underrepresented regions, a novel approach dubbed In-Context Learning (ICL) aims to address these limitations. This paper introduces IM-Context, a method leveraging in-context learning for imbalanced regression tasks, demonstrating its efficacy over conventional in-weight learning methods.

Key Concepts and Problem Setting

Imbalanced data distributions complicate the generalization capabilities of regression models. Existing solutions have primarily revolved around in-weight learning, including sample re-weighting and embedding space regularization techniques, which attempt to smooth label distributions or enforce similarity between samples in feature space. These methods rely on gradient updates to model weights, which inherently limit their ability to generalize in tail regions of the data.

In contrast, in-context learning models adapt to new tasks using context examples without any parameter updates. For a given query input, ICL models leverage a sequence of in-context samples—pairs of inputs and corresponding labels—to generate predictions. This paradigm shift offers a potential solution to the overfitting issues faced by in-weight learning models in minority regions.

Methodology

The IM-Context approach addresses the imbalanced regression challenge by emphasizing localized context. Theoretical analysis reveals that using a large, indiscriminate context can bias models toward majority regions. The proposed strategy mitigates this by considering only the 'closest' in-context samples for a new query, which reduces bias and memory requirements.

Empirical studies validate these theoretical findings. The authors showed that, in dense regions, the error remains stable regardless of context size, while in sparse regions, increasing the context size actually worsens performance. The localized approach, which retrieves neighboring samples from both the original training set and an augmented set (inverse density dataset), consistently demonstrates improved performance.

Results

The IM-Context framework was evaluated on eight imbalanced regression tasks. On AgeDB-DIR and IMDB-WIKI-DIR datasets, in-context learning with the proposed localized approach outperforms state-of-the-art in-weight learning methods across many benchmarks. Particularly impressive are its gains in few-shot regions, where traditional methods typically struggle the most. In one instance, the method reduced the Mean Absolute Error (MAE) by 1.4 points in the AgeDB-DIR few-shot category.

Similarly, for STS-B-DIR, which involves text similarity estimation, the localized in-context learning approach yielded substantial improvements in Mean Squared Error (MSE) across all shot regions. In the tabular datasets, which vary widely in feature size and imbalance degree, the localized in-context learning method outperformed several machine learning baselines, notably reducing errors in medium and few-shot regions.

Implications and Future Directions

The findings underscore the potential of in-context learning in addressing imbalanced regression tasks. Practically, these results suggest a shift in how we approach regression in data-scarce environments, favoring models that can adapt contextually without retraining. This has significant implications for fields like personalized medicine, autonomous driving, and financial forecasting, where data imbalance is common, and accurate predictions are critical.

Theoretically, the paper expands on the understanding of how context size influences error, particularly highlighting the trade-offs in dense versus sparse labels. The localized retrieval strategy offers a pathway to mitigate biases inherent in training data distributions.

Future research could explore the application of IM-Context to more complex regression tasks, such as those with multi-dimensional labels, and investigate how other model variants can further enhance performance. Additionally, studying the impact of different sampling strategies on retrieval accuracy and model performance could yield further insights into optimizing in-context learning methodologies for diverse applications.

Conclusion

This paper makes a compelling case for in-context learning as a robust solution to the perennial issue of label imbalance in regression tasks. The IM-Context framework leverages contextual adaptation, significantly outperforming traditional in-weight learning methods, especially in underrepresented regions. These findings mark a step forward in the practical application and theoretical understanding of in-context learning, promising broader, more effective deployment in various high-stake domains.