Search Results for author:
Found 7 papers, 2 papers with code
Intersectional Fairness: A Fractal Approach
The problem can be approached by looking at different protected attributes (e. g., ethnicity, gender, etc) independently, but fairness for individual protected attributes does not imply intersectional fairness.
Evaluating explainability for machine learning predictions using model-agnostic metrics
Rapid advancements in artificial intelligence (AI) technology have brought about a plethora of new challenges in terms of governance and regulation.
Uncovering Bias in Face Generation Models
The contribution of this work is a novel analysis covering architectures and embedding spaces for fine-grained understanding of bias over three approaches: generators, attribute modifier, and post-processing bias mitigators.
Local Law 144: A Critical Analysis of Regression Metrics
The use of automated decision tools in recruitment has received an increasing amount of attention.
QuantNet: Transferring Learning Across Systematic Trading Strategies
The encoder transforms market-specific data into an abstract latent representation that is processed by a global model shared by all markets, while the decoder learns a market-specific trading strategy based on both local and global information from the market-specific encoder and the global model.
Augmenting correlation structures in spatial data using deep generative models
We empirically show the superiority of this approach over conventional ensemble learning approaches and rivaling spatial data augmentation methods, using synthetic and real-world prediction tasks.
Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination
Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion.
