6 citations · 13 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
One system for learning and remembering episodes and rules
Joshua T. S. Hewson, Sabina J. Sloman, Marina Dubova
Humans can learn individual episodes and generalizable rules and also successfully retain both kinds of acquired knowledge over time. In the cognitive science literature, (1) learn…
Bayesian Active Learning in the Presence of Nuisance Parameters
Sabina J. Sloman, Ayush Bharti, Julien Martinelli +1
In many settings, such as scientific inference, optimization, and transfer learning, the learner has a well-defined objective, which can be treated as estimation of a target parame…
Learning Relevant Contextual Variables Within Bayesian Optimization
Julien Martinelli, Ayush Bharti, Armi Tiihonen +5
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding…
Knowing what to know: Implications of the choice of prior distribution on the behavior of adaptive design optimization
Sabina J. Sloman, Daniel Cavagnaro, Stephen B. Broomell
Adaptive design optimization (ADO) is a state-of-the-art technique for experimental design (Cavagnaro, Myung, Pitt, & Kujala, 2010). ADO dynamically identifies stimuli that, in exp…
A Comparison of Methods for Adaptive Experimentation
Samantha Horn, Sabina J. Sloman
We use a simulation study to compare three methods for adaptive experimentation: Thompson sampling, Tempered Thompson sampling, and Exploration sampling. We gauge the performance o…
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias
Sabina J. Sloman, Daniel M. Oppenheimer, Stephen B. Broomell +1
Bayesian adaptive experimental design is a form of active learning, which chooses samples to maximize the information they give about uncertain parameters. Prior work has shown tha…