ADD: kdd news

Author: msilver22

content/news/kdd26/featured.png

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+---
+title: Our paper titled "Countering Overfitting with Counterfactual Examples" has been accepted at KDD 2026
+summary: We connect two key properties of machine learning models, generalization and explainability.
+date: 2025-12-04
+authors:
+  - student4
+  - student1
+  - collaborator6
+  - director
+tags:
+  - KDD2026
+  - Overfitting
+  - Regularization
+  - Counterfactual examples
+---
+
+In this work, we connect two key properties of machine learning models: **generalization** and **explainability**. Specifically, we show that the more a model overfits its training data, the easier it becomes, on average, to find counterfactual examples (i.e., small input perturbations that change the model's prediction).
+
+Leveraging this observation, we propose **CF-Reg**, a new regularization term that mitigates overfitting by enforcing a sufficient margin between training samples and their counterfactuals.
+
+Our experiments indicate that this strategy not only improves generalization but also produces counterfactual explanations naturally as a by product of the training process.
+
+🔗 The preprint is available at the following [link](https://arxiv.org/pdf/2502.09193?).
+

content/news/kdd26/index.md

@@ -520,9 +520,6 @@
     "ids": [
       "1-learnable-actions-for-nabla-cmh",
       "2-implement-nabla-ocmh",
-      "2-implement-o-nabla-cmh",
-      "3",
-      "3-review-and-implement-scalable-detection-algorithm",
       "3-scalable-community-detection-algorithm",
       "aboutus",
       "code",