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

arXiv: https://arxiv.org/abs/2010.16040 · HTML: https://mlsanity.com/paper/2010.16040/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Materials Representation and Transfer Learning for Multi-Property Prediction](https://mlsanity.com/paper/2106.02225/) (2021)
- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data](https://mlsanity.com/paper/2311.00936/) (2023)
- [Partitioned Graph Convolution Using Adversarial and Regression Networks for Road Travel Speed Prediction](https://mlsanity.com/paper/2103.00067/) (2021)
- [Learning Model-Based Vehicle-Relocation Decisions for Real-Time Ride-Sharing: Hybridizing Learning and Optimization](https://mlsanity.com/paper/2105.13461/) (2021)
- [Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle Relocation and Pricing Decisions](https://mlsanity.com/paper/2111.03204/) (2021)
- [From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction](https://mlsanity.com/paper/2509.10501/) (2025)
- [How Much is a Human Right Worth? ECtHR-NPD: A Benchmark for Predicting Non-Pecuniary Damage Awards](https://mlsanity.com/paper/2609.18908/) (2026)

## Abstract (arXiv)

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.