paper-conference

Can Transformers Learn Full Bayesian Inference in Context?

Arik Reuter, Tim GJ Rudner, Vincent Fortuin, David Rügamer

Data Sharing between Wind Energy Producers: Economic Incentives and Strategic Behavior

Jan Marco Ruiz de Vargas, Annika Schneider, Pierre Pinson, Christoph Goebel

Data-Driven Discovery of Feature Groups in Clinical Time Series

Fedor Sergeev, Manuel Burger, Polina Leshetkina, Vincent Fortuin, Gunnar Rätsch, Rita Kuznetsova

e-SparX: A Graph-Based Artifact Exchange Platform to Accelerate Machine Learning Research in the Energy Systems Community

Annika Schneider, Lukas Schüttler, Jan Marco Ruiz de Vargas, Michael Eichelbeck, Christoph Goebel

ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods

Michal Kmicikiewicz, Vincent Fortuin, Ewa Szczurek

Sparse Gaussian Neural Processes

Tommy Rochussen, Vincent Fortuin

What Actually Matters for Materials Discovery: Pitfalls and Recommendations in Bayesian Optimization

Tristan Cinquin, Stanley Lo, Felix Strieth-Kalthoff, Alan Aspuru-Guzik, Geoff Pleiss, Robert Bamler, Tim GJ Rudner, Vincent Fortuin, Agustinus Kristiadi

Counterfactual Reasoning with Knowledge Graph Embeddings

Lena Zellinger, Andreas Stephan, Benjamin Roth

FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning

Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler

Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models

Emre Onal, Klemens Flöge, Emma Caldwell, Arsen Sheverdin, Vincent Fortuin