Sanath Kumar Krishnamurthy, Susan Athey
arXiv 23 Feb 2020 · Machine Learning
arXiv:2002.09814 · PDF · DOI · OpenAlex · Extracted main text
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.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Li, L., Chu, W., Langford, J., and Schapire, R. E (2010) A contextual-bandit approach to personalized news article recommendation | 0.843 | 3 | 3 | 100% |
| 2 | Bastani, H. and Bayati, M (2015) Online decision-making with high-dimensional covariates | 0.644 | 3 | 2 | 67% |
| 3 | Abbasi-Yadkori, Y., Pál, D., and Szepesvári, C (2011) Improved algorithms for linear stochastic bandits | 0.511 | 2 | 2 | 50% |
| 4 | Abbasi-Yadkori, Y., Pal, D., and Szepesvari, C (2012) Online-to-confidence-set conversions and application to sparse stochastic bandits | 0.511 | 2 | 1 | 100% |
| 5 | Bouneffouf, D., Rish, I., Cecchi, G. A., and Féraud, R (2017) Context attentive bandits: Contextual bandit with restricted context | 0.405 | 1 | 1 | 100% |
| 6 | Bühlmann, P. and Van De Geer, S (2011) Statistics for high-dimensional data: methods, theory and applications | 0.000 | 1 | 1 | 0% |
| 7 | Ye, Y (1999) Approximating global quadratic optimization with convex quadratic constraints | 0.000 | 1 | 1 | 0% |
Showing the top 7 of 7 scored citations.