Gjergji Kasneci
Professor
Studies transparency, robustness, bias and fairness of machine learning algorithms, together with their ethical, legal and societal implications.
Gjergji Kasneci's lab at TUM works on trustworthy & safe ML. 56 papers since 2023, 18 at top venues, mostly AAAI/ACM AIES and Findings of EMNLP. 8 alumni.
At a glance
- papers, 2023–2026
- 15/ year
- at T1 venues
- 32%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 8
Papers by yearfrom DBLP, 2023–2026
- 8 papers2026 · T1 3 · collecting
- 24 papers2025 · T1 6
- 12 papers2024 · T1 3
- 12 papers2023 · T1 6
T1 venuesOtherVenues: AAAI/ACM AIES, Findings of EMNLP, ICLR, ICML, AAAI
Research focuspaper titles that use each topic's words
- Trustworthy & safe ML4 papers
- transparency1 paper
- together0 papers
- ethical0 papers
- social sciences0 papers
Paper list
From DBLP.
20268 papers
- Findings of ACLAnalysing the Safety Pitfalls of Steering Vectors (opens in a new tab)
- FAccTAuditing LLM-Governed Social Robots with Culture-Specific Moral Gradients (opens in a new tab)
- Trans. Mach. Learn. Res.COunterfactual Reasoning for Temporal EXplanations: Plausible and Robust Explanations for EEG-Based Seizure Detection (opens in a new tab)
- Trans. Mach. Learn. Res.High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability (opens in a new tab)
- ICLR (T1)Reinforcement Unlearning via Group Relative Policy Optimization (opens in a new tab)
- ACL (T1)SAGE: Sparse Adaptive Guidance for Dependency-Aware Tabular Data Generation (opens in a new tab)
- AI EthicsThe safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems (opens in a new tab)
- ACL (T1)Where Paths Split: Localized, Calibrated Control of Moral Reasoning in Large Language Models (opens in a new tab)
202524 papers
- AAAI/ACM AIESAdoption of Explainable Natural Language Processing: Perspectives from Industry and Academia on Practices and Challenges (opens in a new tab)
- CHILALPEC: A Comprehensive Evaluation Framework and Dataset for Machine Learning-Based Arousal Detection in Clinical Practice (opens in a new tab)
- Trans. Mach. Learn. Res.Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers (opens in a new tab)
- ICANNCan LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study (opens in a new tab)
- ACM Trans. Appl. Percept.Can You Tell Real from Fake Face Images? Perception of Computer-Generated Faces by Humans (opens in a new tab)
- Findings of EMNLPCURE: Controlled Unlearning for Robust Embeddings - Mitigating Conceptual Shortcuts in Pre-Trained Language Models (opens in a new tab)
- EMNLP (T1)Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs (opens in a new tab)
- xAIEvalxNLP: A Framework for Benchmarking Post-Hoc Explainability Methods on NLP Models (opens in a new tab)
- Trans. Mach. Learn. Res.Explanation Shift: How Did the Distribution Shift Impact the Model? (opens in a new tab)
- Findings of EMNLPFrom Confidence to Collapse in LLM Factual Robustness (opens in a new tab)
- FAccTGender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods (opens in a new tab)
- AAAI/ACM AIESGermanPartiesQA: Benchmarking Commercial Large Language Models and AI Companions for Political Alignment and Sycophancy (opens in a new tab)
202412 papers
- IEEE Big DataAdversarial Reweighting Guided by Wasserstein Distance to Achieve Demographic Parity (opens in a new tab)
- IEEE Trans. Neural Networks Learn. Syst.Deep Neural Networks and Tabular Data: A Survey (opens in a new tab)
- ECAIEnhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule (opens in a new tab)
- INLGExplainability Meets Text Summarization: A Survey (opens in a new tab)
- AAAI (T1)I Prefer Not to Say: Protecting User Consent in Models with Optional Personal Data (opens in a new tab)
- LREC/COLINGIs Crowdsourcing Breaking Your Bank? Cost-Effective Fine-Tuning of Pre-trained Language Models with Proximal Policy Optimization (opens in a new tab)
- Findings of ACLP-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models (opens in a new tab)
- Findings of EMNLPThe Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI (opens in a new tab)
- TPAMI (T1)Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations (opens in a new tab)
- xAITowards Non-adversarial Algorithmic Recourse (opens in a new tab)
- DELTAUnderstanding Knowledge Drift in LLMs Through Misinformation (opens in a new tab)
- KDD (T1)Unifying Evolution, Explanation, and Discernment: A Generative Approach for Dynamic Graph Counterfactuals (opens in a new tab)
202312 papers
- PKDD/ECML WorkshopsAdapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes (opens in a new tab)
- AIEDCan You Solve This on the First Try? - Understanding Exercise Field Performance in an Intelligent Tutoring System (opens in a new tab)
- IEEE Big DataCausal Fairness-Guided Dataset Reweighting using Neural Networks (opens in a new tab)
- DSPCould Human Gaze Augment Detectors of Synthetic Images? (opens in a new tab)
- ICDM WorkshopCounterfactual Explanation for Regression via Disentanglement in Latent Space (opens in a new tab)
- Int. J. Data Sci. Anal.DeepTLF: robust deep neural networks for heterogeneous tabular data (opens in a new tab)
- NeurIPS (T1)Gaussian Membership Inference Privacy (opens in a new tab)
- AAAI (T1)Interventional SHAP Values and Interaction Values for Piecewise Linear Regression Trees (opens in a new tab)
- ICLR (T1)Language Models are Realistic Tabular Data Generators (opens in a new tab)
- ICLR (T1)On the Trade-Off between Actionable Explanations and the Right to be Forgotten (opens in a new tab)
- ICLR (T1)Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse (opens in a new tab)
- UAI (T1)When are post-hoc conceptual explanations identifiable? (opens in a new tab)
Where alumni went8 listed
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Computer Science Doctorate (CIT)
- Round
- Rolling (position-based)
- Opens
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- GRE
- See program page
- English
- Very good written and spoken English; no minimum test score
- Fee
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Confirmed on the program page.
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