Study maps research streams in biodiversity finance, identifies key areas.
problem Biodiversity loss and need for finance to reverse trends.
method Quantitative bibliometric analysis of 189,456 references.
result Identifies eight primary research streams in biodiversity finance.
EcoCast predicts biodiversity risks using satellite data and citizen science records.
problem Unprecedented shifts in species distributions due to climate change and habitat loss.
method Spatio-temporal model using sequence-based transformers and continual learning.
result Promising improvements in forecasting bird species distributions compared to Random Forest.
Study quantifies firm risks from nature decline, showing significant equity losses.
problem Estimating the financial impact of nature deterioration on companies.
method Developed metrics (Country Degradation Index, Nature Risk Score) and assessed five environmental hazards.
result Global equities lose 26.8% in a nature decline scenario, with worst firms losing 75%.
Ecological systems have a high level of complexity combined with stability and rich biodiversity. Recently, the analysis of their properties and evolution has been pushed forward on a basis of concept of mutualistic networks that provides a detailed understanding of their features being linked to a high nestedness of t…
We are losing biodiversity at an unprecedented scale and in many cases, we do not even know the basic data for the species. Traditional methods for wildlife monitoring are inadequate. Development of new computer vision tools enables the use of images as the source of information about wildlife. Social media is the rich…
Biodiversity conservation depends on accurate, up-to-date information about wildlife population distributions. Motion-activated cameras, also known as camera traps, are a critical tool for population surveys, as they are cheap and non-intrusive. However, extracting useful information from camera trap images is a cumber…
Proposes a new framework for environmental CVA with robust wrong-way risk.
problem Limited operational implementations of translating environmental scenarios into CVA.
method Three components: hazard rate mapping, tail generators, and KL divergence-based wrong-way risk bound.
result Nature CVAs can vary significantly across different ecosystem generators.
Biodiversity monitoring using audio recordings is achievable at a truly global scale via large-scale deployment of inexpensive, unattended recording stations or by large-scale crowdsourcing using recording and species recognition on mobile devices. The ability, however, to reliably identify vocalising animal species is…
DeepMaxent uses neural networks to improve species distribution models.
problem Sampling biases and lack of absence data in presence-only observations.
method DeepMaxent employs neural networks to learn shared features among species using the maximum entropy principle.
result DeepMaxent outperforms traditional methods in predicting species distributions, especially in unevenly sampled regions.
Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Speci…
Adversarial meta-learning computes Gamma-minimax estimators for vague prior knowledge.
problem Estimating parameters with vague prior knowledge.
method Adversarial meta-learning algorithms for Gamma-minimax estimators.
result Convergence guarantees and neural network class for selection.
In many developing countries intellectual property infringement and the commerce of pirate goods is an entrepreneurial activity. Digital piracy is very often the only media for having access to music, cinema, books and software. At the same time, bio-prospecting and infringement of indigenous knowledge rights by intern…
A discrete system's heterogeneity is measured by the Rényi heterogeneity family of indices (also known as Hill numbers or Hannah--Kay indices), whose units are {the numbers equivalent}. Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require {a priori} (A) categorical partitioning and …
CLSB models system dynamics from cross-sectional data with population-level regularization.
problem Challenges in modeling system dynamics from limited cross-sectional samples and heterogeneous individual behaviors.
method Introduces CLSB framework for learning dynamics, regularized for population-level temporal variations.
result Empirically superior in single-cell sequencing data analyses, e.g., simulating cell development and drug response.
Machine learning predicts plant phenotypes from soil microbiome data.
problem Predicting plant phenotypes from soil microbiome data.
method Two models (random forest and Bayesian neural network) were used to predict plant phenotypes from soil properties and microbial population density.
result Human decisions and normalization strategies significantly impact model performance.
Populations of species in ecosystems are often constrained by availability of resources within their environment. In effect this means that a growth of one population, needs to be balanced by comparable reduction in populations of others. In neutral models of biodiversity all populations are assumed to change increment…
Framework improves marine mammal monitoring in noisy underwater environments.
problem Underwater bioacoustic monitoring challenges due to overlapping calls and variable noise.
method Multi-step attention-guided framework with segmentation and mid-level fusion.
result Improved signal discrimination, reduced false positives, reliable representations.
Efficiently infers graph edges from genetic similarity data in landscape genetics.
problem Inferring unknown graph edges from genetic similarity data in a heterogeneous landscape.
method Developed an efficient first-order optimization method to solve the inverse landscape genetics problem.
result Our method provides fast and reliable convergence, significantly outperforming existing heuristics.
Deep learning identifies frog species and detects new ones.
problem Identifying and detecting new species in morphologically similar groups.
method Machine learning, specifically deep neural networks, applied to frog hind limb skin texture.
result Deep neural networks can classify images into known species and new classes.
Study identifies key drivers and spatio-temporal trends of extreme Mediterranean wildfires.
problem Understanding and predicting the impacts of climate change on wildfire activity.
method Statistical deep-learning model combining meteorological, land cover, and orographic data.
result Vapour-pressure deficit significantly affects wildfire occurrence, while air temperature and drought affect spread.
Introduces Fitzpatrick losses, tighter than Fenchel-Young losses.
problem Improving loss functions for machine learning.
method Introduces Fitzpatrick losses based on the Fitzpatrick function.
result Fitzpatrick losses are tighter than Fenchel-Young losses.
We study losses for binary classification and class probability estimation and extend the understanding of them from margin losses to general composite losses which are the composition of a proper loss with a link function. We characterise when margin losses can be proper composite losses, explicitly show how to determ…
We present the Tamed Cross Entropy (TCE) loss function, a robust derivative of the standard Cross Entropy (CE) loss used in deep learning for classification tasks. However, unlike other robust losses, the TCE loss is designed to exhibit the same training properties than the CE loss in noiseless scenarios. Therefore, th…
Unified surrogate loss framework for multi-label learning with strong consistency guarantees.
problem Improving consistency and accounting for label correlations in multi-label learning.
method Introducing multi-label logistic loss and extending it to comprehensive multi-label comp-sum losses, proving strong consistency guarantees for any multi-label loss.
result Unified surrogate loss framework benefiting from strong consistency guarantees for any multi-label loss.
This paper introduces new loss functions for balanced multi-class classification.
problem Balancing class imbalance in multi-class classification.
method Introduces two new surrogate loss families: GLA and GCA.
result GCA losses offer stronger theoretical guarantees in imbalanced settings.
We present α-loss, α∈[1,∞], a tunable loss function for binary classification that bridges log-loss (α=1) and 0-1 loss (α=∞). We prove that α-loss has an equivalent margin-based form and is classification-calibrated, two desirable properties for a good surrogate loss function for the ideal y…
This work broadens calibeating to various proper losses using Bregman divergence.
problem Calibration for a wide range of proper losses.
method Regret minimization and Bregman divergence approach.
result U-calibration results for a family of Tsallis losses with logarithmic regret and dimension independence.
This work generalizes calibeating for a broader range of proper losses using Bregman divergence.
problem Calibration for a wide range of proper losses beyond Brier and log loss.
method Regret minimization based on Bregman divergence for a family of proper losses.
result U-calibration results for a family of Tsallis losses with logarithmic regret and dimension independence.
Proposes squentropy loss for improved classification accuracy and model calibration.
problem Theoretical and empirical evidence for cross-entropy loss is lacking.
method Introduces squentropy loss as the sum of cross-entropy and average square loss over incorrect classes.
result Squentropy loss outperforms cross-entropy and rescaled square losses in classification accuracy and model calibration.
New loss function calibrates WW-hinge loss for multiclass SVM.
problem WW-hinge loss not calibrated with 0-1 loss.
method Introduced ordered partition loss and proved WW-hinge loss is calibrated.
result WW-hinge loss is calibrated with ordered partition loss.
The study analyzes a model for aggregate losses with dependent and overdispersed inter-losses times.
problem Analyzing aggregate loss models with dependent and overdispersed inter-losses times.
method The study uses a two-state Markovian arrival process (MAP2) and a Markov renewal process to model the inter-losses times. Severities are modeled using a heavy-tailed, double-Pareto Lognormal distribution. The model is estimated via direct maximization of the likelihood function.
result The model with dependence and overdispersion in inter-losses times leads to higher capital charges compared to a Poisson process.
Symmetric losses improve classifier robustness from corrupted labels.
problem Improving classifier performance from corrupted labels.
method Symmetric losses that satisfy a certain condition.
result Symmetric losses enhance robust classification from corrupted labels.
Two new algorithms improve performance in adversarial bandits with unbounded losses.
problem Adversarial Multi-Armed Bandits with unbounded losses.
method Developed UMAB-NN and UMAB-G for non-negative and general unbounded losses respectively.
result UMAB-NN achieves the first adaptive and scale-free regret bound for non-negative unbounded losses.
Paper explores connections between loss functions and consistency in binary classification and regression.
problem Consistency in binary classification and regression applications.
method Characterization of conformable loss functions and derivation of a new Huber-type loss function.
result Margin-based loss functions are equivalent to loss functions of squared standardized logistic regression residuals.
Paper introduces a new topological loss for better convergence.
problem Optimizing topological losses for model's desired topological behavior.
method Introduces a new regularized topology-aware loss function.
result Guarantees efficient optimization of the new loss function.
Novel loss functions improve decision tree learning from noisy data.
problem Training decision trees with noisy labels.
method Introducing distribution losses and a new negative exponential loss.
result The negative exponential loss leads to efficient and robust decision tree learning.
Theoretical analysis of cross-entropy loss functions and their robustness.
problem Guarantees for using cross-entropy as a surrogate loss function.
method Theoretical analysis of a broad family of loss functions, including cross-entropy.
result First H-consistency bounds for comp-sum losses and smooth adversarial comp-sum losses. This paper improves operational risk modeling by selecting better loss severity distributions.
problem Inconsistent regulatory capital calculations due to changing loss severity distribution families.
method Presented truncation probability estimates and a consistent quantile scoring function for selection criteria. Also, recommended collecting loss frequencies below the minimum reporting threshold.
result More stable regulatory capital calculations through better selection of loss severity distributions.
This paper improves loss functions for deep learning with noisy labels.
problem Training deep neural networks with noisy labels.
method The paper introduces a normalization technique to make any loss function robust to noisy labels and proposes a framework called Active Passive Loss (APL) to combine robust loss functions.
result The proposed APL framework consistently outperforms state-of-the-art methods, especially under high noise rates.
Classification is the most important process in data analysis. However, due to the inherent non-convex and non-smooth structure of the zero-one loss function of the classification model, various convex surrogate loss functions such as hinge loss, squared hinge loss, logistic loss, and exponential loss are introduced. T…
We study cross-country GDP losses due to financial crises in terms of frequency (number of loss events per period) and severity (loss per occurrence). We perform the Loss Distribution Approach (LDA) to estimate a multi-country aggregate GDP loss probability density function and the percentiles associated to extreme eve…
The paper proves deep learning can be robust with certain loss functions.
problem The robustness of deep learning models under flawed data.
method Empirical-risk minimization with unbounded, Lipschitz-continuous loss functions.
result These loss functions provide efficient prediction under minimal data assumptions.
The paper explores transferability of adversarial examples between convex and 01 loss models, finding non-transferability due to different decision boundaries caused by outliers.
problem Transferability of adversarial examples between convex and 01 loss models.
method Empirical study of transferability between linear 01 loss and convex (hinge) loss models, and between neural networks with different activation functions.
result Adversarial examples are non-transferable between convex and 01 loss models due to different decision boundaries caused by outliers.
Symmetrizes loss functions to improve neural network robustness against noisy labels.
problem Designing robust loss functions for noisy labels in neural networks.
method Symmetrization of multi-class loss functions, focusing on cross-entropy and unhinged loss.
result The multi-class unhinged loss is the unique convex symmetric loss under suitable assumptions.
EnsLoss combines multiple loss functions to prevent overfitting in classification.
problem Preventing overfitting in classification models.
method EnsLoss is an ensemble method that combines loss functions, ensuring calibration and consistency.
result EnsLoss improves classification accuracy compared to fixed loss methods.
Quantification of the stationary points and the associated basins of attraction of neural network loss surfaces is an important step towards a better understanding of neural network loss surfaces at large. This work proposes a novel method to visualise basins of attraction together with the associated stationary points…
Study compares metric learning loss functions for speaker verification.
problem Comparing metric learning loss functions for end-to-end speaker verification.
method Cross entropy loss, cosine loss, angular margin loss, center loss, contrastive loss, triplet loss.
result Additive angular margin loss outperforms other loss functions.
In this work, we introduce the {\em average top-k} (\atk) loss as a new aggregate loss for supervised learning, which is the average over the k largest individual losses over a training dataset. We show that the \atk loss is a natural generalization of the two widely used aggregate losses, namely the average loss a…