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We present online boosting algorithms for multiclass classification with bandit feedback, where the learner only receives feedback about the correctness of its prediction.
The weighted majority algorithm
Littlestone, N. and Warmuth, M. K. (1994) · 1994
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A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y. and Schapire, R. E. (1997) · 1997
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Cesa-Bianchi, N. and Lugosi, G. (2006) · 2006
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Efficient bandit algorithms for online multiclass prediction
Kakade, S. M., Shalev-Shwartz, S., and Tewari, A. (2008) · 2008
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An online boosting algorithm with theoretical justifications
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Mukherjee, I. and Schapire, R. E. (2013) · 2013
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Understanding machine learning: From theory to algorithms
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Optimal and adaptive algorithms for online boosting
Beygelzimer, A., Kale, S., and Luo, H. (2015) · 2015
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Multiclass learnability and the erm principle
Daniely, A., Sabato, S., Ben-David, S., and Shalev-Shwartz, S. (2015) · 2015
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Self-organizing feature maps identify proteins critical to learning in a mouse model of down syndrome
Higuera, C., Gardiner, K. J., and Cios, K. J. (2015) · 2015
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Efficient online bandit multiclass learning with O ~ ( T ) \tilde{O}(\sqrt{T}) regret
Beygelzimer, A., Orabona, F., and Zhang, C. (2017) · 2017
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Online multiclass boosting
Jung, Y. H., Goetz, J., and Tewari, A. (2017) · 2017
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Logistic regression: The importance of being improper
Foster, D. J., Kale, S., Luo, H., Mohri, M., and Sridharan, K. (2018) · 2018
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Online boosting algorithms for multi-label ranking
Jung, Y. H. and Tewari, A. (2018) · 2018
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