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Experiment Planning with Function Approximation
Aldo Pacchiano · Jonathan Lee · Emma Brunskill

Fri Jul 28 06:50 PM -- 07:00 PM (PDT) @
Event URL: https://openreview.net/forum?id=dRW67bW45G »

We study the problem of experiment planning with function approximation in contextual bandit problems. In settings where there is a significant overhead to deploying adaptive algorithms; for example, when the execution of the data collection policies is required to be distributed, or a human in the loop is needed to implement these policies, producing in advance a set of policies for data collection is paramount. We study the setting where a large dataset of contexts -but not rewards- is available and may be used by the learner to design an effective data collection strategy. Although when rewards are linear this problem has been well studied, results are still missing for more complex reward models. In this work we propose two experiment planning strategies compatible with function approximation, first an eluder planning and sampling procedure that can recover optimality guarantees depending on the eluder dimension of the reward function class, and second we show the uniform sampler has competitive rates in the setting where the number of actions is small.

Author Information

Aldo Pacchiano (Broad Institute)
Jonathan Lee (Stanford University)
Emma Brunskill (Stanford University)
Emma Brunskill

Emma Brunskill is an associate tenured professor in the Computer Science Department at Stanford University. Brunskill’s lab aims to create AI systems that learn from few samples to robustly make good decisions and is part of the Stanford AI Lab, the Stanford Statistical ML group, and AI Safety @Stanford. Brunskill has received a NSF CAREER award, Office of Naval Research Young Investigator Award, a Microsoft Faculty Fellow award and an alumni impact award from the computer science and engineering department at the University of Washington. Brunskill and her lab have received multiple best paper nominations and awards both for their AI and machine learning work (UAI best paper, Reinforcement Learning and Decision Making Symposium best paper twice) and for their work in Ai of education (Intelligent Tutoring Systems Conference, Educational Data Mining conference x3, CHI).

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