1k citations · 2.2k across the 96 of their papers we have counts for
52 papers · 1 filter
Correcting or Continuing? What On-Policy Distillation Learns from Student Errors
Shuo Yang, Zheyu Zhang, Mahito Sugiyama +1
On-policy distillation (OPD) trains a student on its own rollouts under a frozen teacher, mitigating the distribution mismatch of off-policy supervision. While prior work has prima…
Relational Learning as World Modeling
Zheyu Zhang, Gjergji Kasneci
Relational learning typically predicts from a database state, but decision making asks a different question: how will that state change under an action? In operational systems, the…
High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability
Sven Jacob, Bardh Prenkaj, Weijia Shao +1
Change point detection (CPD) identifies abrupt and significant changes in sequential data, with applications in human activity recognition, financial markets, cybersecurity, manufa…
Beyond Binary Rewards: A Comparative Study of Reward Design for Reinforcement Unlearning
Efstratios Zaradoukas, Davide Gabrielli, Bardh Prenkaj +1
Machine unlearning seeks to selectively remove specific knowledge from trained language models without full retraining, a growing necessity under privacy regulations such as GDPR a…
Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
Zheyu Zhang, Shuo Yang, Bardh Prenkaj +1
Generative tabular augmentation is appealing in data-scarce domains, yet the prevailing focus on distributional fidelity does not reliably translate into better downstream models.…
SAGE: Sparse Adaptive Guidance for Dependency-Aware Tabular Data Generation
Shuo Yang, Zheyu Zhang, Bardh Prenkaj +1
Generating high-fidelity synthetic tabular data remains a critical challenge for enhancing data availability in privacy-sensitive and low-resource domains. Recent approaches levera…
TabSCM: A practical Framework for Generating Realistic Tabular Data
Sven Jacob, Bardh Prenkaj, Weijia Shao +1
Most tabular-data generators match marginal statistics yet ignore causal structure, leading downstream models to learn spurious or unfair patterns. We present TabSCM, a mixed-type…
Reinforcement Unlearning via Group Relative Policy Optimization
Efstratios Zaradoukas, Bardh Prenkaj, Gjergji Kasneci
During pretraining, LLMs inadvertently memorize sensitive or copyrighted data, posing significant compliance challenges under legal frameworks like the GDPR and the EU AI Act. Fulf…
Assessing the Real-World Utility of Explainable AI for Arousal Diagnostics: An Application-Grounded User Study
Stefan Kraft, Andreas Theissler, Vera Wienhausen-Wilke +2
Artificial intelligence (AI) systems increasingly match or surpass human experts in biomedical signal interpretation. However, their effective integration into clinical practice re…
Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs
Shuo Yang, Zheyu Zhang, Bardh Prenkaj +1
Tabular data is critical across diverse domains, yet high-quality datasets remain scarce due to privacy concerns and the cost of collection. Contemporary approaches adopt large lan…
SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification
Shuo Yang, Bardh Prenkaj, Gjergji Kasneci
Shortcut learning undermines model generalization to out-of-distribution data. While the literature attributes shortcuts to biases in superficial features, we show that imbalances…
Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents
Michael Kirchhof, Gjergji Kasneci, Enkelejda Kasneci
Large-language models (LLMs) and chatbot agents are known to provide wrong outputs at times, and it was recently found that this can never be fully prevented. Hence, uncertainty qu…
Graph Inverse Style Transfer for Counterfactual Explainability
Bardh Prenkaj, Efstratios Zaradoukas, Gjergji Kasneci
Counterfactual explainability seeks to uncover model decisions by identifying minimal changes to the input that alter the predicted outcome. This task becomes particularly challeng…
Emergent Abilities in Large Language Models: A Survey
Leonardo Berti, Flavio Giorgi, Gjergji Kasneci
Large Language Models (LLMs) are leading a new technological revolution as one of the most promising research streams toward artificial general intelligence. The scaling of these m…
Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers
Gjergji Kasneci, Enkelejda Kasneci
Feature engineering is crucial for optimizing machine learning model performance, particularly in tabular data classification tasks. Leveraging advancements in natural language pro…
Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule
Xuan Zhao, Klaus Broelemann, Salvatore Ruggieri +1
We introduce an innovative approach to enhancing the empirical risk minimization (ERM) process in model training through a refined reweighting scheme of the training data to enhanc…
ALPEC: A Comprehensive Evaluation Framework and Dataset for Machine Learning-Based Arousal Detection in Clinical Practice
Stefan Kraft, Andreas Theissler, Vera Wienhausen-Wilke +2
Detecting arousals in sleep is essential for diagnosing sleep disorders. However, using Machine Learning (ML) in clinical practice is impeded by fundamental issues, primarily due t…
P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models
Shuo Yang, Chenchen Yuan, Yao Rong +2
A multitude of industries depend on accurate and reasonable tabular data augmentation for their business processes. Contemporary methodologies in generating tabular data revolve ar…
Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers
Tobias Leemann, Alina Fastowski, Felix Pfeiffer +1
We address the critical challenge of applying feature attribution methods to the transformer architecture, which dominates current applications in natural language processing and b…
Towards Non-Adversarial Algorithmic Recourse
Tobias Leemann, Martin Pawelczyk, Bardh Prenkaj +1
The streams of research on adversarial examples and counterfactual explanations have largely been growing independently. This has led to several recent works trying to elucidate th…
Counterfactual Explanation for Regression via Disentanglement in Latent Space
Xuan Zhao, Klaus Broelemann, Gjergji Kasneci
Counterfactual Explanations (CEs) help address the question: How can the factors that influence the prediction of a predictive model be changed to achieve a more favorable outcome…
Adversarial Reweighting Guided by Wasserstein Distance for Bias Mitigation
Xuan Zhao, Simone Fabbrizzi, Paula Reyero Lobo +4
The unequal representation of different groups in a sample population can lead to discrimination of minority groups when machine learning models make automated decisions. To addres…
Causal Fairness-Guided Dataset Reweighting using Neural Networks
Xuan Zhao, Klaus Broelemann, Salvatore Ruggieri +1
The importance of achieving fairness in machine learning models cannot be overstated. Recent research has pointed out that fairness should be examined from a causal perspective, an…
Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes
Bardh Prenkaj, Mario Villaizan-Vallelado, Tobias Leemann +1
We introduce a novel semi-supervised Graph Counterfactual Explainer (GCE) methodology, Dynamic GRAph Counterfactual Explainer (DyGRACE). It leverages initial knowledge about the da…