A key problem in computational sustainability is to understand the
distribution of species across landscapes over time. This question gives rise
to challenging large-scale prediction problems since (i) hundreds of species
have to be simultaneously modeled and (ii) the survey data are usually inflated
with zeros due to the absence of species for a large number of sites. The
problem of tackling both issues simultaneously, which we refer to as the
zero-inflated multi-target regression problem, has not been addressed by
previous methods in statistics and machine learning. In this paper, we propose
a novel deep model for the zero-inflated multi-target regression problem. To
this end, we first model the joint distribution of multiple response variables
as a multivariate probit model and then couple the positive outcomes with a
multivariate log-normal distribution. By penalizing the difference between the
two distributions' covariance matrices, a link between both distributions is
established. The whole model is cast as an end-to-end learning framework and we
provide an efficient learning algorithm for our model that can be fully
implemented on GPUs. We show that our model outperforms the existing
state-of-the-art baselines on two challenging real-world species distribution
datasets concerning bird and fish populations.

本文提出了一种深度模型来解决计算可持续性中的物种分布问题，其中有大量的零填充数据，并且需要同时对数百个物种进行建模，该模型被称为 “零膨胀多目标回归问题”。通过将整个模型作为端到端的学习框架，我们提供了一种有效的学习算法，并在两个实际物种分布的数据集上展示了该模型比现有的最先进基线模型表现更好，其核心是将多响应变量的联合分布建模为多元 Probit 模型。

应用于多物种丰度估计的深度障碍网络零膨胀多目标回归

Deep Hurdle Networks for Zero-Inflated Multi-Target Regression:  Application to Multiple Species Abundance Estimation

We propose a generalization of the best arm identification problem in
stochastic multi-armed bandits (MAB) to the setting where every pull of an arm
is associated with delayed feedback. The delay in feedback increases the
effective sample complexity of standard algorithms, but can be offset if we
have access to partial feedback received before a pull is completed. We propose
a general framework to model the relationship between partial and delayed
feedback, and as a special case we introduce efficient algorithms for settings
where the partial feedback are biased or unbiased estimators of the delayed
feedback. Additionally, we propose a novel extension of the algorithms to the
parallel MAB setting where an agent can control a batch of arms. Our
experiments in real-world settings, involving policy search and hyperparameter
optimization in computational sustainability domains for fast charging of
batteries and wildlife corridor construction, demonstrate that exploiting the
structure of partial feedback can lead to significant improvements over
baselines in both sequential and parallel MAB.

本文研究了在多臂赌博机的延迟反馈场景下，如何利用局部反馈来提高标准算法的样本复杂度。采用模型化的方法探讨了局部反馈和延迟反馈之间的关系，并提出了一种用于处理偏差或无偏差情况下局部反馈的有效算法。另外，还针对并行多臂赌博机提出了一种新的算法扩展。在实际场景中，针对电池快速充电和野生动物走廊建设的计算可持续性领域中的策略搜索和超参数优化等问题的实验表明，利用局部反馈的结构可以显著提高标准算法的性能。

多臂老虎机中带延迟反馈的最佳臂识别

Best arm identification in multi-armed bandits with delayed feedback

Stochastic network design is a general framework for optimizing network
connectivity. It has several applications in computational sustainability
including spatial conservation planning, pre-disaster network preparation, and
river network optimization. A common assumption in previous work has been made
that network parameters (e.g., probability of species colonization) are
precisely known, which is unrealistic in real- world settings. We therefore
address the robust river network design problem where the goal is to optimize
river connectivity for fish movement by removing barriers. We assume that fish
passability probabilities are known only imprecisely, but are within some
interval bounds. We then develop a planning approach that computes the policies
with either high robust ratio or low regret. Empirically, our approach scales
well to large river networks. We also provide insights into the solutions
generated by our robust approach, which has significantly higher robust ratio
than the baseline solution with mean parameter estimates.

提出了一种解决鱼类在河流中障碍物的通过问题的方法，该方法采用鲁棒性网络设计框架，为具有不确定参数的鱼类通过概率分布计算出针对高健壮性比率或低遗憾度的策略，并在大规模河流网络中具有良好的可扩展性和更高的稳定等级。