Jesper Ferkinghoff-Borg — accepted AI-conference papers

2025

Zero-shot protein stability prediction by inverse folding models: a free energy interpretation

Jes Frellsen, Maher M. Kassem, Tone Bengtsen, Lars Olsen, Kresten Lindorff-Larsen, Jesper Ferkinghoff-Borg +1

NeurIPS 2025poster

Inverse folding models have proven to be highly effective zero-shot predictors of protein stability. Despite this success, the link between the amino acid preferences of an inverse folding model and the free-energy considerations underlying thermodynamic stability remains incompletely understood. A…

Cited by 5 Source ↗code ↗Scholar ↗

2024

Batched Energy-Entropy acquisition for Bayesian Optimization

Felix Teufel, Carsten Stahlhut, Jesper Ferkinghoff-Borg

NeurIPS 2024poster

Bayesian optimization (BO) is an attractive machine learning framework for performing sample-efficient global optimization of black-box functions. The optimization process is guided by an acquisition function that selects points to acquire in each round of BO. In batched BO, when multiple points are…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2016

Bayesian Generalised Ensemble Markov Chain Monte Carlo

Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg

AISTATS 2016poster

Bayesian generalised ensemble (BayesGE) is a new method that addresses two major drawbacks of standard Markov chain Monte Carlo algorithms for inference in high-dimensional probability models: inapplicability to estimate the partition function and poor mixing properties. BayesGE uses a Bayesian appr…

Cited by 12 Source ↗PDF ↗Scholar ↗