When forecasting time series with a hierarchical structure, the existing
state of the art is to forecast each time series independently, and, in a
post-treatment step, to reconcile the time series in a way that respects the
hierarchy (Hyndman et al., 2011; Wickramasuriya et al., 2018). We propose a new
loss function that can be incorporated into any maximum likelihood objective
with hierarchical data, resulting in reconciled estimates with confidence
intervals that correctly account for additional uncertainty due to imperfect
reconciliation. We evaluate our method using a non-linear model and synthetic
data on a counterfactual forecasting problem, where we have access to the
ground truth and contemporaneous covariates, and show that we largely improve
over the existing state-of-the-art method.

提出一种新的损失函数，可与任何最大似然目标相结合，并可用于具有层次数据的预测时间序列，从而生成具有置信区间的协调估计，以正确地考虑由于不完美的调节而产生的额外不确定性。

一种用于分层预测的自监督方法，并应用于群体综合控制

A Self-supervised Approach to Hierarchical Forecasting with Applications  to Groupwise Synthetic Controls

Novelty detection is commonly referred to as the discrimination of
observations that do not conform to a learned model of regularity. Despite its
importance in different application settings, designing a novelty detector is
utterly complex due to the unpredictable nature of novelties and its
inaccessibility during the training procedure, factors which expose the
unsupervised nature of the problem. In our proposal, we design a general
framework where we equip a deep autoencoder with a parametric density estimator
that learns the probability distribution underlying its latent representations
through an autoregressive procedure. We show that a maximum likelihood
objective, optimized in conjunction with the reconstruction of normal samples,
effectively acts as a regularizer for the task at hand, by minimizing the
differential entropy of the distribution spanned by latent vectors. In addition
to providing a very general formulation, extensive experiments of our model on
publicly available datasets deliver on-par or superior performances if compared
to state-of-the-art methods in one-class and video anomaly detection settings.
Differently from prior works, our proposal does not make any assumption about
the nature of the novelties, making our work readily applicable to diverse
contexts.

设计了一种基于深度自编码器和参数密度估计器的新颖性检测方法，通过最大似然优化与正常样本重建相结合的方式，成功地将分布差异熵最小化并取得了与现有最先进方法相当或更好的性能，而且不做任何新奇性的假设，可适用于各种不同的场景。

潜空间自回归用于新颖性检测

Latent Space Autoregression for Novelty Detection

We prove that stochastic gradient descent efficiently converges to the global
optimizer of the maximum likelihood objective of an unknown linear
time-invariant dynamical system from a sequence of noisy observations generated
by the system. Even though the objective function is non-convex, we provide
polynomial running time and sample complexity bounds under strong but natural
assumptions. Linear systems identification has been studied for many decades,
yet, to the best of our knowledge, these are the first polynomial guarantees
for the problem we consider.

我们证明了随机梯度下降算法可以高效地收敛于未知线性时不变动态系统的极大似然目标函数的全局极值。虽然该目标函数是非凸的，但我们在强但自然的假设下提供了多项式运行时间和样本复杂度界限。尽管线性系统识别已经研究了许多年，但据我们所知，这是我们所考虑的问题的第一个多项式保证。