Timezone: »
Bayesian approaches to preference elicitation (PE) are particularly attractive due to their ability to explicitly model uncertainty in users' latent utility functions. However, previous approaches to Bayesian PE have ignored the important problem of generalizing from previous users to an unseen user in order to reduce the elicitation burden on new users. In this paper, we address this deficiency by introducing a Gaussian Process (GP) prior over users' latent utility functions on the joint space of user and item features. We learn the hyper-parameters of this GP on a set of preferences of previous users and use it to aid in the elicitation process for a new user. This approach provides a flexible model of a multi-user utility function, facilitates an efficient value of information (VOI) heuristic query selection strategy, and provides a principled way to incorporate the elicitations of multiple users back into the model. We show the effectiveness of our method in comparison to previous work on a real dataset of user preferences over sushi types.
Author Information
Edwin Bonilla (CSIRO's Data61)
Shengbo Guo (ANU -- NICTA)
Scott Sanner (University of Toronto)
More from the Same Authors
-
2022 : Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus »
Yudong Xu · Elias Khalil · Scott Sanner -
2022 Poster: Learning to Follow Instructions in Text-Based Games »
Mathieu Tuli · Andrew Li · Pashootan Vaezipoor · Toryn Klassen · Scott Sanner · Sheila McIlraith -
2021 Poster: Risk-Aware Transfer in Reinforcement Learning using Successor Features »
Michael Gimelfarb · Andre Barreto · Scott Sanner · Chi-Guhn Lee -
2021 Poster: Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models »
Yi Sui · Ga Wu · Scott Sanner -
2019 : Outstanding Contribution Talk: Variational Graph Convolutional Networks »
Edwin Bonilla -
2019 Poster: Structured Variational Inference in Continuous Cox Process Models »
Virginia Aglietti · Edwin Bonilla · Theodoros Damoulas · Sally Cripps -
2015 Poster: Scalable Inference for Gaussian Process Models with Black-Box Likelihoods »
Amir Dezfouli · Edwin Bonilla -
2014 Poster: Extended and Unscented Gaussian Processes »
Daniel M Steinberg · Edwin Bonilla -
2014 Spotlight: Extended and Unscented Gaussian Processes »
Daniel M Steinberg · Edwin Bonilla -
2014 Poster: Automated Variational Inference for Gaussian Process Models »
Trung V Nguyen · Edwin Bonilla -
2013 Workshop: Machine Learning for Sustainability »
Edwin Bonilla · Thomas Dietterich · Theodoros Damoulas · Andreas Krause · Daniel Sheldon · Iadine Chades · J. Zico Kolter · Bistra Dilkina · Carla Gomes · Hugo P Simao -
2012 Poster: Symbolic Dynamic Programming for Continuous State and Observation POMDPs »
Zahra Zamani · Scott Sanner · Pascal Poupart · Kristian Kersting -
2011 Workshop: Choice Models and Preference Learning »
Jean-Marc Andreoli · Cedric Archambeau · Guillaume Bouchard · Shengbo Guo · Kristian Kersting · Scott Sanner · Martin Szummer · Paolo Viappiani · Onno Zoeter -
2011 Poster: Improving Topic Coherence with Regularized Topic Models »
David Newman · Edwin Bonilla · Wray Buntine -
2007 Poster: Multi-task Gaussian Process Prediction »
Edwin Bonilla · Kian Ming A Chai · Chris Williams -
2007 Spotlight: Multi-task Gaussian Process Prediction »
Edwin Bonilla · Kian Ming A Chai · Chris Williams