onsider the great anti-Bayesian Cosma Shalizi. He's shown that the use of a prior is really equivalent to a method of smoothing, of regularization on your hypothesis space, trading off (frequentist) bias and variance.

It seems odd to interpret this point as anti-Bayesian. To me it seems pro-Bayesian: it means that whenever you use a regularizer you're actually doing Bayesian inference. Any method that depends on a regularizer is open to the same critique of subjectivity to which Bayesian methods are vulnerable. Two frequentists using different regularizers will come to different conclusions based on the same evidence, and the choice of a regularizer is hardly inevitable or dictated by the problem.

If you have a link to a paper that contains anti-Bayesian arguments by Shalizi, I would be interested in reading it.