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Sebastian Bordt
Sebastian Bordt
University of Tübingen
uni-tuebingen.de üzerinde doğrulanmış e-posta adresine sahip - Ana Sayfa
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Post-hoc explanations fail to achieve their purpose in adversarial contexts
S Bordt, M Finck, E Raidl, U von Luxburg
ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022
862022
Chatgpt participates in a computer science exam
S Bordt, U von Luxburg
arXiv preprint arXiv:2303.09461, 2023
652023
From Shapley values to generalized additive models and back
S Bordt, U von Luxburg
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
552023
A bandit model for human-machine decision making with private information and opacity
S Bordt, U Von Luxburg
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
22*2022
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
S Bordt, H Nori, V Rodrigues, B Nushi, R Caruana
Conference on Language Modeling (COLM), 2024
202024
Which models have perceptually-aligned gradients? an explanation via off-manifold robustness
S Srinivas, S Bordt, H Lakkaraju
Neural information processing systems (NeurIPS), 2024
132024
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data
S Bordt, H Nori, R Caruana
NeurIPS Workshops, 2023
132023
The Manifold Hypothesis for Gradient-Based Explanations
S Bordt, U Upadhyay, Z Akata, U von Luxburg
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023
112023
Llms understand glass-box models, discover surprises, and suggest repairs
BJ Lengerich, S Bordt, H Nori, ME Nunnally, Y Aphinyanaphongs, ...
arXiv preprint arXiv:2308.01157, 2023
72023
Data Science with LLMs and Interpretable Models
S Bordt, B Lengerich, H Nori, R Caruana
XAI4Sci Workshop at AAAI-24, 2024
52024
Recovery guarantees for kernel-based clustering under non-parametric mixture models
LC Vankadara, S Bordt, U von Luxburg, D Ghoshdastidar
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
32021
Statistics without interpretation: a sober look at explainable machine learning
S Bordt, U von Luxburg
arXiv preprint arXiv:2402.02870, 2024
22024
How much can we forget about Data Contamination?
S Bordt, S Srinivas, V Boreiko, U von Luxburg
arXiv preprint arXiv:2410.03249, 2024
12024
Explainable machine learning and its limitations
S Bordt
University of Tübingen, PhD Thesis, 2023
2023
Sistem, işlemi şu anda gerçekleştiremiyor. Daha sonra yeniden deneyin.
Makaleler 1–14