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From Pseudorandomness to Multi-Group Fairness and Back
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We identify and explore connections between the recent literature on multi-group fairness for prediction algorithms and the pseudorandomness notions of leakage-resilience and graph regularity. We frame our investigation using new variants of multicalibration based on statistical distance and closely related to the concept of outcome indistinguishability. Adopting this perspective leads us not only to new, more efficient algorithms for multicalibration, but also to our graph theoretic results and a proof of a novel hardcore lemma for real-valued functions.
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Computationally Efficient Collaborative Communication Via Regularity-Based Coarsening
If an optimal (even intractable) protocol achieves utility α in k bits, a polynomial-time algorithm can find a protocol achieving α−ε using 2^{O(k)}/ε^2 bits, and this is tight up to a constant in the exponent.
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