3 citations · 9 across the 9 of their papers we have counts for
11 papers
CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes
Yuche Gao, Arik Reuter, Siyuan Guo +3
Causal foundation models (CFMs) amortise causal inference over priors of synthetic structural causal models (SCMs), predicting the effect of an experiment on a specific variable. H…
Use What You Know: Causal Foundation Models with Partial Graphs
Arik Reuter, Anish Dhir, Cristiana Diaconu +6
Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions. Recently proposed Causal Foundation Models (CFMs) promise a more unified a…
Do-PFN: In-Context Learning for Causal Effect Estimation
Jake Robertson, Arik Reuter, Siyuan Guo +3
Estimation of causal effects is critical to a range of scientific disciplines. Existing methods for this task either require interventional data, knowledge about the ground truth c…
Position: The Future of Bayesian Prediction Is Prior-Fitted
Samuel Müller, Arik Reuter, Noah Hollmann +2
Training neural networks on randomly generated artificial datasets yields Bayesian models that capture the prior defined by the dataset-generating distribution. Prior-data Fitted N…
Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks
Anton Thielmann, Arik Reuter, Benjamin Saefken
In recent years, deep neural networks have showcased their predictive power across a variety of tasks. Beyond natural language processing, the transformer architecture has proven e…
Can Transformers Learn Full Bayesian Inference in Context?
Arik Reuter, Tim G. J. Rudner, Vincent Fortuin +1
Transformers have emerged as the dominant architecture in the field of deep learning, with a broad range of applications and remarkable in-context learning (ICL) capabilities. Whil…
Mambular: A Sequential Model for Tabular Deep Learning
Anton Frederik Thielmann, Manish Kumar, Christoph Weisser +3
The analysis of tabular data has traditionally been dominated by gradient-boosted decision trees (GBDTs), known for their proficiency with mixed categorical and numerical features.…
Neural Additive Image Model: Interpretation through Interpolation
Arik Reuter, Anton Thielmann, Benjamin Saefken
Understanding how images influence the world, interpreting which effects their semantics have on various quantities and exploring the reasons behind changes in image-based predicti…
Probabilistic Topic Modelling with Transformer Representations
Arik Reuter, Anton Thielmann, Christoph Weisser +2
Topic modelling was mostly dominated by Bayesian graphical models during the last decade. With the rise of transformers in Natural Language Processing, however, several successful…
GPTopic: Dynamic and Interactive Topic Representations
Arik Reuter, Bishnu Khadka, Anton Thielmann +3
Topic modeling seems to be almost synonymous with generating lists of top words to represent topics within large text corpora. However, deducing a topic from such list of individua…
Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence
Anton Thielmann, Quentin Seifert, Arik Reuter +2
Extracting and identifying latent topics in large text corpora has gained increasing importance in Natural Language Processing (NLP). Most models, whether probabilistic models simi…