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Preferential Multi-Attribute Bayesian Optimization with Application to Exoskeleton Personalization
Raul Astudillo · Amy Li · Maegan Tucker · Chu Xin Cheng · Aaron Ames · Yisong Yue

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in The Many Facets of Preference-Based Learning »
Event URL: https://openreview.net/forum?id=Y7saIzVsgR »

Preferential Bayesian optimization (PBO) is a framework for optimization of a decision-maker's (DM's) latent preferences. Existing work in PBO assumes these preferences can be encoded by a single latent utility function, which is then estimated from ordinal preference feedback over design variables. In practice, however, it is often challenging for DMs to provide such feedback reliably, leading to poor performance. This is especially true when multiple conflicting latent attributes govern the DM's preferences. For example, in exoskeleton personalization, users' preferences over gait designs are influenced by stability and walking speed, which can conflict with each other. We posit this is a primary reason why inconsistent preferences are often observed in practice. To address this challenge, we propose a framework for preferential multi-attribute Bayesian optimization, where the goal is to help DMs efficiently explore the Pareto front of their preferences over attributes. Within this framework, we propose a Thompson sampling-based strategy to select new queries and show it performs well across three test problems, including a simulated exoskeleton gait personalization task.

Author Information

Raul Astudillo (California Institute of Technology)
Amy Li (Caltech)
Maegan Tucker (California Institute of Technology)
Chu Xin Cheng (California Institute of Technology)
Aaron Ames (Caltech)
Yisong Yue (Caltech & Latitude AI)
Yisong Yue

Yisong Yue is a Professor of Computing and Mathematical Sciences at Caltech and (via sabbatical) a Principal Scientist at Latitude AI. His research interests span both fundamental and applied pursuits, from novel learning-theoretic frameworks all the way to deep learning deployed in autonomous driving on public roads. His work has been recognized with multiple paper awards and nominations, including in robotics, computer vision, sports analytics, machine learning for health, and information retrieval. At Latitude AI, he is working on machine learning approaches to motion planning for autonomous driving.

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