7 papers · 1 filter
Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings
Lotta Mäkinen, Jorge Loría, Samuel Kaski
Meta-learning methods perform well on new within-distribution tasks but often fail when adapting to out-of-distribution target tasks, where transfer from source tasks can induce ne…
Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs
Zachris Björkman, Jorge Loría, Sophie Wharrie +1
Bayesian causal discovery benefits from prior information elicited from domain experts, and in heterogeneous domains any prior knowledge would be badly needed. However, so far prio…
Epistemic Errors of Imperfect Multitask Learners When Distributions Shift
Sabina J. Sloman, Michele Caprio, Samuel Kaski
Uncertainty-aware machine learners, such as Bayesian neural networks, output a quantification of uncertainty instead of a point prediction. We provide uncertainty-aware learners wi…
Robust and Computation-Aware Gaussian Processes
Marshal Arijona Sinaga, Julien Martinelli, Samuel Kaski
Gaussian processes (GPs) are widely used for regression and optimization tasks such as Bayesian optimization (BO) due to their expressiveness and principled uncertainty estimates.…
Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization
Minttu Alakuijala, Ya Gao, Georgy Ananov +4
As the general capabilities of artificial intelligence (AI) agents continue to evolve, their ability to learn to master multiple complex tasks through experience remains a key chal…
Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model
Hans Moen, Vishnu Raj, Andrius Vabalas +4
Health registers contain rich information about individuals' health histories. Here our interest lies in understanding how individuals' health trajectories evolve in a nationwide l…