Robustness of Bayesian pool-based active learning against prior misspecification Conference

Cuong, NV, Ye, N, Lee, WS. (2016). Robustness of Bayesian pool-based active learning against prior misspecification . 1512-1518.

cited authors

  • Cuong, NV; Ye, N; Lee, WS

abstract

  • We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. In both the average and worst cases of the maximum coverage setting, we prove that all α-approximate algorithms are robust (i.e., near α-approximate) if the utility is Lipschitz continuous in the prior. We further show that robustness may not be achieved if the utility is non-Lipschitz. This suggests we should use a Lipschitz utility for AL if robustness is required. For the minimum cost setting, we can also obtain a robustness result for approximate AL algorithms. Our results imply that many commonly used AL algorithms are robust against perturbed priors. We then propose the use of a mixture prior to alleviate the problem of prior misspecification. We analyze the robustness of the uniform mixture prior and show experimentally that it performs reasonably well in practice.

publication date

  • January 1, 2016

start page

  • 1512

end page

  • 1518