Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalanced Regression

• URL: http://arxiv.org/abs/2506.07033v1
• Date: Sun, 08 Jun 2025 07:53:03 GMT
• Title: Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalanced Regression
• Authors: Yung-Chien Wang, Kuang-Da Wang, Wei-Yao Wang, Wen-Chih Peng,
• Abstract summary: We propose MATI: Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalance Regression.<n>We evaluate MATI on four real-world datasets, including house pricing, bike sharing, and age prediction.<n>On average across these three test distributions, MATI achieved a 7.1% improvement in MAE compared to existing methods.
• Score: 12.35924469567586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tabular data serve as a fundamental and ubiquitous representation of structured information in numerous real-world applications, e.g., finance and urban planning. In the realm of tabular imbalanced applications, data imbalance has been investigated in classification tasks with insufficient instances in certain labels, causing the model's ineffective generalizability. However, the imbalance issue of tabular regression tasks is underexplored, and yet is critical due to unclear boundaries for continuous labels and simplifying assumptions in existing imbalance regression work, which often rely on known and balanced test distributions. Such assumptions may not hold in practice and can lead to performance degradation. To address these issues, we propose MATI: Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalance Regression, featuring two key innovations: (i) the Region-Aware Mixture Expert, which adopts a Gaussian Mixture Model to capture the underlying related regions. The statistical information of each Gaussian component is then used to synthesize and train region-specific experts to capture the unique characteristics of their respective regions. (ii) Test-Time Self-Supervised Expert Aggregation, which dynamically adjusts region expert weights based on test data features to reinforce expert adaptation across varying test distributions. We evaluated MATI on four real-world tabular imbalance regression datasets, including house pricing, bike sharing, and age prediction. To reflect realistic deployment scenarios, we adopted three types of test distributions: a balanced distribution with uniform target frequencies, a normal distribution that follows the training data, and an inverse distribution that emphasizes rare target regions. On average across these three test distributions, MATI achieved a 7.1% improvement in MAE compared to existing methods.

Related papers

• A Distributionally-Robust Framework for Nuisance in Causal Effect Estimation [1.3053649021965603]
  Causal inference requires evaluating models on balanced distributions between treatment and control groups.<n>Most conventional statistical methods address this distribution shift through inverse probability weighting (IPW)<n>We decompose the error generalization to isolate these issues--propensity ambiguity and statistical instability--and address them through an adversarial loss function.
  arXiv  Detail & Related papers  (2025-05-23T10:34:28Z)
• Local distribution-based adaptive oversampling for imbalanced regression [0.0]
  Imbalanced regression occurs when continuous target variables have skewed distributions, creating sparse regions.<n>We propose LDAO (Local Distribution-based Adaptive Oversampling), a novel data-level approach that avoids categorizing individual samples as rare or frequent.<n> LDAO achieves a balanced representation across the entire target range while preserving the inherent statistical structure within each local distribution.
  arXiv  Detail & Related papers  (2025-04-19T14:36:41Z)
• Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced Regression [2.435853975142516]
  In real-world regression tasks, datasets frequently exhibit imbalanced distributions, characterized by a scarcity of data in high-complexity regions and an abundance in low-complexity areas.<n>We introduce a novel concept of Imbalanced Regression, which takes into account both the complexity of the problem and the density of data points, extending beyond traditional definitions that focus only on data density.<n>We propose Error Distribution Smoothing (EDS) as a solution to tackle imbalanced regression, effectively selecting a representative subset from the dataset to reduce redundancy while maintaining balance and representativeness.
  arXiv  Detail & Related papers  (2025-02-04T12:40:07Z)
• Histogram Approaches for Imbalanced Data Streams Regression [1.8385275253826225]
  Imbalanced domains pose a significant challenge in real-world predictive analytics, particularly in the context of regression.<n>This study introduces histogram-based sampling strategies to overcome this constraint.<n> Comprehensive experiments on synthetic and real-world benchmarks demonstrate that HistUS and HistOS substantially improve rare-case prediction accuracy.
  arXiv  Detail & Related papers  (2025-01-29T11:03:02Z)
• Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing [55.791818510796645]
  We aim to develop models that generalize well to any diverse test distribution, even if the latter deviates significantly from the training data. Various approaches like domain adaptation, domain generalization, and robust optimization attempt to address the out-of-distribution challenge. We adopt a more conservative perspective by accounting for the worst-case error across all sufficiently diverse test distributions within a known domain.
  arXiv  Detail & Related papers  (2024-10-08T12:26:48Z)
• Identifying and Mitigating Social Bias Knowledge in Language Models [52.52955281662332]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases.<n>FAST surpasses state-of-the-art baselines with superior debiasing performance.<n>This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler [29.395855812763617]
  We propose AdapTable, a framework for adapting machine learning models to target data without accessing source data.<n>AdapTable operates in two stages: 1) calibrating model predictions using a shift-aware uncertainty calibrator, and 2) adjusting these predictions to match the target label distribution with a label distribution handler.<n>Our results demonstrate AdapTable's ability to handle various real-world distribution shifts, achieving up to a 16% improvement on the dataset.
  arXiv  Detail & Related papers  (2024-07-15T15:02:53Z)
• Uncertainty Voting Ensemble for Imbalanced Deep Regression [20.176217123752465]
  In this paper, we introduce UVOTE, a method for learning from imbalanced data. We replace traditional regression losses with negative log-likelihood, which also predicts sample-wise aleatoric uncertainty. We show that UVOTE consistently outperforms the prior art, while at the same time producing better-calibrated uncertainty estimates.
  arXiv  Detail & Related papers  (2023-05-24T14:12:21Z)
• DELTA: degradation-free fully test-time adaptation [59.74287982885375]
  We find that two unfavorable defects are concealed in the prevalent adaptation methodologies like test-time batch normalization (BN) and self-learning. First, we reveal that the normalization statistics in test-time BN are completely affected by the currently received test samples, resulting in inaccurate estimates. Second, we show that during test-time adaptation, the parameter update is biased towards some dominant classes.
  arXiv  Detail & Related papers  (2023-01-30T15:54:00Z)
• Causal Balancing for Domain Generalization [95.97046583437145]
  We propose a balanced mini-batch sampling strategy to reduce the domain-specific spurious correlations in observed training distributions. We provide an identifiability guarantee of the source of spuriousness and show that our proposed approach provably samples from a balanced, spurious-free distribution.
  arXiv  Detail & Related papers  (2022-06-10T17:59:11Z)
• General Greedy De-bias Learning [163.65789778416172]
  We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
  arXiv  Detail & Related papers  (2021-12-20T14:47:32Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.