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Found 8 papers, 6 papers with code
Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium
The persistent challenge of bias in machine learning models necessitates robust solutions to ensure parity and equal treatment across diverse groups, particularly in classification tasks.
LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns.
Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning
This method involves using a fixed LLM to understand and process the semantic content of the prompt through zero-shot capabilities.
FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation
Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet challenges persist in achieving model interpretability and optimizing performance.
Through a fair looking-glass: mitigating bias in image datasets
In this study, we present a fast and effective model to de-bias an image dataset through reconstruction and minimizing the statistical dependence between intended variables.
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the problem of drug–target interaction prediction.
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Distraction is All You Need for Fairness
There is an abundance of evidence suggesting that these models could contain or even amplify the bias present in the data on which they are trained, inherent to their objective function and learning algorithms; Many researchers direct their attention to this issue in different directions, namely, changing data to be statistically independent, adversarial training for restricting the capabilities of a particular competitor who aims to maximize parity, etc.
TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks
In the unconstrained case, i. e. when the model is only trained in the first phase and is only meant to generate accurate data following the same joint probability distribution of the real data, the results show that the model beats state-of-the-art GANs proposed in the literature to produce synthetic tabular data.
