74 citations · 370 across the 44 of their papers we have counts for
47 papers
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
Maksym Tretiakov, Sarah Filippi, Sarah Lucie Filipp +4
Many machine learning methods aim to approximate the lower-dimensional manifold on which the data lives. A desirable feature of such methods is that they should capture the epistem…
Data-Driven Discovery of Feature Groups in Clinical Time Series
Fedor Sergeev, Manuel Burger, Polina Leshetkina +3
Clinical time series data are critical for patient monitoring and predictive modeling. These time series are typically multivariate and often comprise hundreds of heterogeneous fea…
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information
Fedor Sergeev, Paola Malsot, Gunnar Rätsch +1
Knowing which features of a multivariate time series to measure and when is a key task in medicine, wearables, and robotics. Better acquisition policies can reduce costs while main…
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
Tristan Cinquin, Marvin Pförtner, Vincent Fortuin +2
Laplace approximations are popular techniques for endowing deep networks with epistemic uncertainty estimates as they can be applied without altering the predictions of the trained…
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?
Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian +3
Bayesian optimization (BO) is an integral part of automated scientific discovery -- the so-called self-driving lab -- where human inputs are ideally minimal or at least non-blockin…
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models
Emre Onal, Klemens Flöge, Emma Caldwell +2
Fine-tuned Large Language Models (LLMs) often suffer from overconfidence and poor calibration, particularly when fine-tuned on small datasets. To address these challenges, we propo…