Zobrazeno 1 - 10
of 187
pro vyhledávání: '"von Luxburg, Ulrike"'
In explainable machine learning, global feature importance methods try to determine how much each individual feature contributes to predicting the target variable, resulting in one importance score for each feature. But often, predicting the target v
Externí odkaz:
http://arxiv.org/abs/2410.23772
The leakage of benchmark data into the training data has emerged as a significant challenge for evaluating the capabilities of large language models (LLMs). In this work, we use experimental evidence and theoretical estimates to challenge the common
Externí odkaz:
http://arxiv.org/abs/2410.03249
In sensitive contexts, providers of machine learning algorithms are increasingly required to give explanations for their algorithms' decisions. However, explanation receivers might not trust the provider, who potentially could output misleading or ma
Externí odkaz:
http://arxiv.org/abs/2407.13281
Autor:
Bordt, Sebastian, von Luxburg, Ulrike
In the rapidly growing literature on explanation algorithms, it often remains unclear what precisely these algorithms are for and how they should be used. We argue that this is because explanation algorithms are often mathematically complex but don't
Externí odkaz:
http://arxiv.org/abs/2402.02870
The success of over-parameterized neural networks trained to near-zero training error has caused great interest in the phenomenon of benign overfitting, where estimators are statistically consistent even though they interpolate noisy training data. W
Externí odkaz:
http://arxiv.org/abs/2305.14077
Autor:
Bordt, Sebastian, von Luxburg, Ulrike
We asked ChatGPT to participate in an undergraduate computer science exam on ''Algorithms and Data Structures''. The program was evaluated on the entire exam as posed to the students. We hand-copied its answers onto an exam sheet, which was subsequen
Externí odkaz:
http://arxiv.org/abs/2303.09461
This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today's scientific challenges are characterised by complexity. Interconnected natural, tech
Externí odkaz:
http://arxiv.org/abs/2303.04217
Network-based analyses of dynamical systems have become increasingly popular in climate science. Here we address network construction from a statistical perspective and highlight the often ignored fact that the calculated correlation values are only
Externí odkaz:
http://arxiv.org/abs/2211.02888
Autor:
Klepper, Solveig, von Luxburg, Ulrike
Graph auto-encoders are widely used to construct graph representations in Euclidean vector spaces. However, it has already been pointed out empirically that linear models on many tasks can outperform graph auto-encoders. In our work, we prove that th
Externí odkaz:
http://arxiv.org/abs/2211.01858
Regression on observational data can fail to capture a causal relationship in the presence of unobserved confounding. Confounding strength measures this mismatch, but estimating it requires itself additional assumptions. A common assumption is the in
Externí odkaz:
http://arxiv.org/abs/2211.01903