3 citations · 4 across the 6 of their papers we have counts for
7 papers
Enhancing Assessment of Self-Consistency in LLM Explanations using Perturbation Strength
Phuong Q. Le, Kemal Kurniawan, Jey Han Lau
Prior work has examined the self-consistency of LLM-generated explanations using surface-level perturbation methods. However, the strength of these perturbations is not explicitly…
Shortcut Mitigation via Spurious-Positive Samples
Phuong Quynh Le, Jörg Schlötterer, Sari Sadiya +2
Shortcut mitigation strategies commonly rely on training data annotations, group-balanced held-out data or the presence of all groups, i.e., all combinations of (spurious) attribut…
Invariant Learning with Annotation-free Environments
Phuong Quynh Le, Christin Seifert, Jörg Schlötterer
Invariant learning is a promising approach to improve domain generalization compared to Empirical Risk Minimization (ERM). However, most invariant learning methods rely on the assu…
An XAI-based Analysis of Shortcut Learning in Neural Networks
Phuong Quynh Le, Jörg Schlötterer, Christin Seifert
Machine learning models tend to learn spurious features - features that strongly correlate with target labels but are not causal. Existing approaches to mitigate models' dependence…
Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations
Phuong Quynh Le, Jörg Schlötterer, Christin Seifert
Machine learning models are known to learn spurious correlations, i.e., features having strong relations with class labels but no causal relation. Relying on those correlations lea…
Is Last Layer Re-Training Truly Sufficient for Robustness to Spurious Correlations?
Phuong Quynh Le, Jörg Schlötterer, Christin Seifert
Models trained with empirical risk minimization (ERM) are known to learn to rely on spurious features, i.e., their prediction is based on undesired auxiliary features which are str…
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling
Akash Srivastava, Yamini Bansal, Yukun Ding +15
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the…