9 papers
Representative, Informative, and De-Amplifying: Requirements for Robust Bayesian Active Learning under Model Misspecification
Roubing Tang, Sabina J. Sloman, Samuel Kaski
In many science and industry settings, a central challenge is designing experiments under time and budget constraints. Bayesian Optimal Experimental Design (BOED) is a paradigm to…
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…
CREDO: Epistemic-Aware Conformalized Credal Envelopes for Regression
Luben M. C. Cabezas, Sabina J. Sloman, Bruno M. Resende +3
Conformal prediction delivers prediction intervals with distribution-free coverage, but its intervals can look overconfident in regions where the model is extrapolating, because st…
Robust Experimental Design via Generalised Bayesian Inference
Yasir Zubayr Barlas, Sabina J. Sloman, Samuel Kaski
Bayesian optimal experimental design is a principled framework for conducting experiments that leverages Bayesian inference to quantify how much information one can expect to gain…
Proxy-informed Bayesian transfer learning with unknown sources
Sabina J. Sloman, Julien Martinelli, Samuel Kaski
Generalization outside the scope of one's training data requires leveraging prior knowledge about the effects that transfer, and the effects that don't, between different data sour…
Automating the Practice of Science -- Opportunities, Challenges, and Implications
Sebastian Musslick, Laura K. Bartlett, Suyog H. Chandramouli +11
Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches e…