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In this paper we empirically evaluate biased methods for alpha-divergenceminimization. In particular, we focus on how the bias affects the finalsolutions found, and how this depends on the dimensionality of the problem. Wefind that (i) solutions returned by these methods appear to be strongly biasedtowards minimizers of the traditional "exclusive" KL-divergence, KL(q||p), and(ii) in high dimensions, an impractically large amount of computation is neededto mitigate this bias and obtain solutions that actually minimize thealpha-divergence of interest.

Empirical Evaluation of Biased Methods for Alpha Divergence Minimization