We study the problem of classifier derandomization in machine learning: given
a stochastic binary classifier $f: X \to [0,1]$, sample a deterministic
classifier $\hat{f}: X \to \{0,1\}$ that approximates the output of $f$ in
aggregate over any data distribution. Recent work revealed how to efficiently
derandomize a stochastic classifier with strong output approximation
guarantees, but at the cost of individual fairness -- that is, if $f$ treated
similar inputs similarly, $\hat{f}$ did not. In this paper, we initiate a
systematic study of classifier derandomization with metric fairness guarantees.
We show that the prior derandomization approach is almost maximally
metric-unfair, and that a simple ``random threshold'' derandomization achieves
optimal fairness preservation but with weaker output approximation. We then
devise a derandomization procedure that provides an appealing tradeoff between
these two: if $f$ is $\alpha$-metric fair according to a metric $d$ with a
locality-sensitive hash (LSH) family, then our derandomized $\hat{f}$ is, with
high probability, $O(\alpha)$-metric fair and a close approximation of $f$. We
also prove generic results applicable to all (fair and unfair) classifier
derandomization procedures, including a bias-variance decomposition and
reductions between various notions of metric fairness.

该论文研究了机器学习中分类器去随机化的问题，并提出了同时具有度量公平性和输出近似的去随机化方法。

度量 - 公平分类器去随机化

Metric-Fair Classifier Derandomization

We present COD3S, a novel method for generating semantically diverse
sentences using neural sequence-to-sequence (seq2seq) models. Conditioned on an
input, seq2seq models typically produce semantically and syntactically
homogeneous sets of sentences and thus perform poorly on one-to-many sequence
generation tasks. Our two-stage approach improves output diversity by
conditioning generation on locality-sensitive hash (LSH)-based semantic
sentence codes whose Hamming distances highly correlate with human judgments of
semantic textual similarity. Though it is generally applicable, we apply COD3S
to causal generation, the task of predicting a proposition's plausible causes
or effects. We demonstrate through automatic and human evaluation that
responses produced using our method exhibit improved diversity without
degrading task performance.

COD3S 是一种使用神经序列到序列模型生成语义多样句子的新方法，通过在本地敏感哈希（LSH）的语义句子代码上进行生成，提高了输出多样性，特别适用于因果生成任务。经过自动和人工评估，我们证明了使用我们的方法生成的响应具有改进的多样性而不降低任务性能。