Many high-stakes applications require machine learning models that protect
user privacy and provide well-calibrated, accurate predictions. While
Differential Privacy (DP) is the gold standard for protecting user privacy,
standard DP mechanisms typically significantly impair performance. One approach
to mitigating this issue is pre-training models on simulated data before DP
learning on the private data. In this work we go a step further, using
simulated data to train a meta-learning model that combines the Convolutional
Conditional Neural Process (ConvCNP) with an improved functional DP mechanism
of Hall et al. [2013] yielding the DPConvCNP. DPConvCNP learns from simulated
data how to map private data to a DP predictive model in one forward pass, and
then provides accurate, well-calibrated predictions. We compare DPConvCNP with
a DP Gaussian Process (GP) baseline with carefully tuned hyperparameters. The
DPConvCNP outperforms the GP baseline, especially on non-Gaussian data, yet is
much faster at test time and requires less tuning.

通过使用模拟数据来训练元学习模型，将 Convolutional Conditional Neural Process (ConvCNP) 与改进的 DP 机制相结合，从而提供准确、良好校准的预测模型，并在非高斯数据上优于 DP 高斯过程（GP）基线模型。

基于元学习的噪声感知差分隐私回归

Noise-Aware Differentially Private Regression via Meta-Learning

Several individualization methods have recently been proposed to estimate a
subject's Head-Related Transfer Function (HRTF) using convenient input
modalities such as anthropometric measurements or pinnae photographs. There
exists a need for adaptively correcting the estimation error committed by such
methods using a few data point samples from the subject's HRTF, acquired using
acoustic measurements or perceptual feedback. To this end, we introduce a
Convolutional Conditional Neural Process meta-learner specialized in HRTF error
interpolation. In particular, the model includes a Spherical Convolutional
Neural Network component to accommodate the spherical geometry of HRTF data. It
also exploits potential symmetries between the HRTF's left and right channels
about the median axis. In this work, we evaluate the proposed model's
performance purely on time-aligned spectrum interpolation grounds under a
simplified setup where a generic population-mean HRTF forms the initial
estimates prior to corrections instead of individualized ones. The trained
model achieves up to 3 dB relative error reduction compared to state-of-the-art
interpolation methods despite being trained using only 85 subjects. This
improvement translates up to nearly a halving of the data point count required
to achieve comparable accuracy, in particular from 50 to 28 points to reach an
average of -20 dB relative error per interpolated feature. Moreover, we show
that the trained model provides well-calibrated uncertainty estimates.
Accordingly, such estimates can inform the sequential decision problem of
acquiring as few correcting HRTF data points as needed to meet a desired level
of HRTF individualization accuracy.

使用便利的输入方式（如人体测量或耳廓照片）估计主体的头部相关转移函数（HRTF）的个体化方法存在误差，该研究提出了一种卷积条件神经过程元学习器，专门用于 HRTF 误差插值，并在时间对齐的频谱插值场景中实现了高达 3 dB 的相对误差降低，大约减少了一半的数据点数量达到相似的准确性。此外，该模型提供了良好校准的不确定性估计，可应用于最小化获取足够的 HRTF 数据点以满足所需个体化准确性水平的决策问题。

使用球形神经过程元学习器进行 HRTF 插值

HRTF Interpolation using a Spherical Neural Process Meta-Learner

We introduce the Convolutional Conditional Neural Process (ConvCNP), a new
member of the Neural Process family that models translation equivariance in the
data. Translation equivariance is an important inductive bias for many learning
problems including time series modelling, spatial data, and images. The model
embeds data sets into an infinite-dimensional function space as opposed to a
finite-dimensional vector space. To formalize this notion, we extend the theory
of neural representations of sets to include functional representations, and
demonstrate that any translation-equivariant embedding can be represented using
a convolutional deep set. We evaluate ConvCNPs in several settings,
demonstrating that they achieve state-of-the-art performance compared to
existing NPs. We demonstrate that building in translation equivariance enables
zero-shot generalization to challenging, out-of-domain tasks.

本研究介绍了卷积条件神经过程 (ConvCNP)，它是神经过程家族中的新成员，能够建模数据中的平移等变性。研究表明，加入平移等变性的建模有助于更好地处理时间序列、空间数据和图像，而且卷积深度集能够实现任何平移等变嵌入。研究还证明，ConvCNP 在多个设置中取得了良好的性能，并且能够在挑战性的任务中实现零样本泛化。