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Survey Bandits with Regret Guarantees

Sanath Kumar Krishnamurthy, Susan Athey

arXiv 23 Feb 2020 · Machine Learning

arXiv:2002.09814 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We consider a variant of the contextual bandit problem. In standard contextual bandits, when a user arrives we get the user's complete feature vector and then assign a treatment (arm) to that user. In a number of applications (like healthcare), collecting features from users can be costly. To address this issue, we propose algorithms that avoid needless feature collection while maintaining strong regret guarantees.

Citation extraction

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appendix boundary found by appendix_command · 65% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Li, L., Chu, W., Langford, J., and Schapire, R. E (2010) A contextual-bandit approach to personalized news article recommendation0.84333100%
2Bastani, H. and Bayati, M (2015) Online decision-making with high-dimensional covariates0.6443267%
3Abbasi-Yadkori, Y., Pál, D., and Szepesvári, C (2011) Improved algorithms for linear stochastic bandits0.5112250%
4Abbasi-Yadkori, Y., Pal, D., and Szepesvari, C (2012) Online-to-confidence-set conversions and application to sparse stochastic bandits0.51121100%
5Bouneffouf, D., Rish, I., Cecchi, G. A., and Féraud, R (2017) Context attentive bandits: Contextual bandit with restricted context0.40511100%
6Bühlmann, P. and Van De Geer, S (2011) Statistics for high-dimensional data: methods, theory and applications0.000110%
7Ye, Y (1999) Approximating global quadratic optimization with convex quadratic constraints0.000110%

Showing the top 7 of 7 scored citations.