Papers with empirical risk minimization
Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization (2023.findings-acl)
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| Challenge: | Existing methods for minimizing the worst-case loss of annotated groups are lacking in practice due to expensive annotations and privacy issues. |
| Approach: | They propose a distributionally robust optimization framework that relaxes group identification into direct parameterization by using an interactive training mode. |
| Outcome: | The proposed method outperforms state-of-the-art methods on synthetic and real-world text classification tasks. |
FTFT: Efficient and Robust Fine-Tuning by Transferring Training Dynamics (2025.coling-main)
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| Challenge: | Despite the success of fine-tuning Pre-trained Language Models, they remain susceptible to out-of-distribution input. |
| Approach: | They propose a novel approach that fine-tunes Pre-trained Language Models by transFerring Training dynamics (FTFT) FTFT uses more efficient reference models and aggressive early stopping . |
| Outcome: | The proposed approach improves the robustness of fine-tuned PLMs while reducing training costs. |
Improved Training of Deep Text Clustering (2023.findings-emnlp)
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| Challenge: | Existing methods for deep clustering optimization with shallow models have limited performance due to poor power of feature learning. |
| Approach: | They propose a general deep clustering optimization method that leverages information feedback to construct generalized labels to optimize the deep model. |
| Outcome: | The proposed method reduces the impact of noise on the clustering process by using correlation relationship between the samples. |
The Impact of Differential Privacy on Group Disparity Mitigation (2024.findings-naacl)
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| Challenge: | a recent study evaluated the impact of differential privacy on fairness across four tasks. |
| Approach: | They evaluate the impact of differential privacy on fairness across four diverse tasks . they train (,)-differentially private models with empirical risk minimization . |
| Outcome: | The proposed model shows that differential privacy increases performance differences between groups . the model also reduces performance differences in the robust setting . |
Distributionally Robust Multilingual Machine Translation (2021.emnlp-main)
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| Challenge: | Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, but the data imbalance hinders it from performing uniformly across language pairs. |
| Approach: | They propose a distributionally robust optimization objective which minimizes the worst-case expected loss over the set of language pairs. |
| Outcome: | The proposed learning objective outperforms baseline methods on three sets of languages and shows that it is cost-effective and efficient. |
Learn from Failure: Causality-guided Contrastive Learning for Generalizable Implicit Hate Speech Detection (2025.coling-main)
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| Challenge: | Existing methods for detecting implicit hate speech rely on correlations between class labels and spurious attributes, which leads to poor performance on data lacking correlations. |
| Approach: | They propose a causality-guided contrastive learning approach to enhance the generalizability of implicit hate speech detection by aligning the representations of samples with the same class but opposite spurious attributes. |
| Outcome: | The proposed approach outperforms current state-of-the-art methods in cross-domain generalization on multiple implicit hate speech datasets. |
Mitigating Spurious Correlations in Text Classification Using Latent Space Geometry (2026.acl-long)
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| Challenge: | Existing models rely on predictive shortcuts that hold in training data but break under distribution shifts, leading to large performance drops for minority groups. |
| Approach: | They propose a framework that transforms abstract biases into interpretable geometric anchors without auxiliary classifiers by manipulating latent space geometry. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines and improves worst-group accuracy by over 20% on the CivilComments dataset. |