Delayed Reward Bernoulli Bandits: Optimal Policy and Predictive Meta-Algorithm PARDI

Pilarski, Sebastian; Pilarski, Slawomir; Varró, Dániel

doi:10.1109/tai.2021.3117743

Cited by 4 publications

(2 citation statements)

References 27 publications

(63 reference statements)

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“…Williamson et al [64] identify the existing gap between the theory and clinical practice for Bayesian response-adaptive procedures by considering whether responses are intermediately available or delayed, respectively. However, these findings may not apply to the missing data problem in the context of responseadaptive designs, since missing values can be viewed as a very extreme form of delay where the outcomes would never be available [43]. In other words, the problem of missing data is distinct and has not received as much attention as the problem of delayed outcomes.…”

Section: Introductionmentioning

confidence: 94%

Some performance considerations when using multi-armed bandit algorithms in the presence of missing data

Chen,

Lee,

Villar

et al. 2022

Preprint

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When using multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, the simplest approach is to ignore missing outcomes and continue to sample following the bandit algorithm. We investigate the impact of missing data on several bandit algorithms via a simulation study assuming the rewards are missing at random. We focus on two-armed bandit algorithms with binary outcomes in the context of patient allocation for clinical trials with relatively small sample sizes. However, our results can apply to other applications of bandit algorithms where missing data is expected to occur. We assess the resulting operating characteristics, including the expected reward (i.e., allocation results). Different probabilities of missingness in both arms are considered. The key finding of our work is that when using the simplest strategy of ignoring missing data, the corresponding impact on the performance of multi-armed bandit strategies varies according to their way of balancing the exploration-exploitation trade-off. Algorithms that are geared towards exploration continue to assign samples to the arm with more missing responses, and this arm is perceived as the superior arm by the algorithm. By contrast, algorithms that are geared towards exploitation would do the opposite and not assign samples to the arms with more missing responses. Furthermore, for algorithms focusing more on exploration, we illustrate that the problem of missing responses can be alleviated using a simple mean imputation approach.

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Section: Introductionmentioning

confidence: 94%

Some performance considerations when using multi-armed bandit algorithms in the presence of missing data

Chen,

Lee,

Villar

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Williamson et al [ 29 ] identify the existing gap between the theory and clinical practice for Bayesian response-adaptive procedures by considering whether responses are intermediately available or delayed, respectively. However, these findings may not apply to the missing data problem in the context of response-adaptive designs, since missing values can be viewed as a very extreme form of delay where the outcomes would never be available [ 30 ]. In other words, the problem of missing data is distinct and has not received as much attention as the problem of delayed outcomes.…”

Section: Introductionmentioning

confidence: 99%

Some performance considerations when using multi-armed bandit algorithms in the presence of missing data

et al. 2022

View full text Add to dashboard Cite

When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample according to the original bandit algorithm, ignoring missing outcomes. We investigate the impact on performance of this approach to deal with missing data for several bandit algorithms through an extensive simulation study assuming the rewards are missing at random. We focus on two-armed bandit algorithms with binary outcomes in the context of patient allocation for clinical trials with relatively small sample sizes. However, our results apply to other applications of bandit algorithms where missing data is expected to occur. We assess the resulting operating characteristics, including the expected reward. Different probabilities of missingness in both arms are considered. The key finding of our work is that when using the simplest strategy of ignoring missing data, the impact on the expected performance of multi-armed bandit strategies varies according to the way these strategies balance the exploration-exploitation trade-off. Algorithms that are geared towards exploration continue to assign samples to the arm with more missing responses (which being perceived as the arm with less observed information is deemed more appealing by the algorithm than it would otherwise be). In contrast, algorithms that are geared towards exploitation would rapidly assign a high value to samples from the arms with a current high mean irrespective of the level observations per arm. Furthermore, for algorithms focusing more on exploration, we illustrate that the problem of missing responses can be alleviated using a simple mean imputation approach.

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Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy

Aziz

Anderton

Jamieson

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

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Delayed Reward Bernoulli Bandits: Optimal Policy and Predictive Meta-Algorithm PARDI

Cited by 4 publications

References 27 publications

Some performance considerations when using multi-armed bandit algorithms in the presence of missing data

Some performance considerations when using multi-armed bandit algorithms in the presence of missing data

Some performance considerations when using multi-armed bandit algorithms in the presence of missing data

Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy

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