In short, both Michael and Suresh think they are a good idea.

I agree with much of their motivations, but, based on my experience in both non-blinded (e.g., STOC/FOCS) and blinded (e.g., CRYPTO) conferences as both reviewer and author, I do not think double blind reviewing is a good fit for theoretical computer science.

Second, you often have the case where the reviewer knows who the authors are, and has some history with them, even if it’s not a formal conflict, but the program committee member does not know this information.

We complement these results by considering the problem's approximability and show that, with respect to $\Delta^*$, the problem admits an algorithm which for any $\epsilon 0$ runs in time $(tw/\epsilon)^$ and returns a solution with exactly the desired number of colors that approximates the optimal $\Delta^*$ within $(1 \epsilon)$.

We also give a $(tw)^$ algorithm which achieves the desired $\Delta^*$ exactly while 2-approximating the minimum value of $\chi_d$.

Our approach is based on a new general framework relating deterministic search and deterministic approximate counting, which we believe may find further applications.

Michael Mitzenmacher points to two posts of Suresh Venkatasubramanian on the issue of so called “double blind reviews” (i.e., anonymous submissions) in theory conferences.

If anonymous submissions don’t work well for theory conferences, does it mean we have to just have to accept biases? I believe there are a number of things we could attempt.

First, while completely anonymizing submissions might not work well, we could try to make the author names less prominent, for example by having them in the last page of the submissions instead of the first, and not showing them in the conference software. As I mentioned in my tips for future FOCS/STOC chairs, one potential approach is to tag papers by authors who never had a prior STOC/FOCS paper (one could possibly also tag papers by authors from under-represented groups).We give a deterministic algorithm which, given an $n$-variable $\mathrm(n)$-clause CNF formula $F$ that has at least $\varepsilon 2^n$ satisfying assignments, runs in time $n^$ for $\varepsilon \ge 1/\mathrm(n)$ and outputs a satisfying assignment of $F$.Prior to our work the fastest known algorithm for this problem was simply to enumerate over all seeds of a pseudorandom generator for CNFs; using the best known PRGs for CNFs [DETT10], this takes time $n^$ even for constant $\varepsilon$.More generally, these days much of theoretical CS is moving to the model where papers are first posted online, and by the time they are submitted to a conference they have circulated quite a bit around the relevant experts.Posting papers online is very good for science and should be encouraged, as it allows fast dissemination of results, but it does make the anonymous submission model obsolete.As expected, this hardness can be extended to larger values of $\chi_d$ for most of these parameters, with one surprising exception: we show that the problem is FPT parameterized by feedback vertex set for any $\chi_d \ge 2$, and hence 2-coloring is the only hard case for this parameter.

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