Search for: All records

Learning fair representation in deep learning is essential to mitigate discriminatory outcomes and enhance trustworthiness. However previous research has been commonly established on inappropriate assumptions prone to unrealistic counterfactuals and performance degradation. Although some proposed alternative approaches such as employing correlation-aware causal graphs or proxies for mutual information these methods are less practical and not applicable in general. In this work we propose FAir DisEntanglement with Sensitive relevance (FADES) a novel approach that leverages conditional mutual information from the information theory perspective to address these challenges. We employ sensitive relevant code to direct correlated information between target labels and sensitive attributes by imposing conditional independence allowing better separation of the features of interest in the latent space. Utilizing an intuitive disentangling approach FADES consistently achieves superior performance and fairness both quantitatively and qualitatively with its straightforward structure. Specifically the proposed method outperforms existing works in downstream classification and counterfactual generations on various benchmarks. 

Time-series generation has crucial practical significance for decision-making under uncertainty. Existing methods have various limitations like accumulating errors over time, significantly impacting downstream tasks. We develop a novel generation method, DT-VAE, that incorporates generalizable domain knowledge, is mathematically justified, and significantly outperforms existing methods by mitigating error accumulation through a cumulative difference learning mechanism. We evaluate the performance of DT-VAE on several downstream tasks using both semi-synthetic and real time-series datasets, including benchmark datasets and our newly curated COVID-19 hospitalization datasets. The COVID-19 datasets enrich existing resources for time-series analysis. Additionally, we introduce Diverse Trend Preserving (DTP), a time-series clustering-based evaluation for direct and interpretable assessments of generated samples, serving as a valuable tool for evaluating time-series generative models. 

Fairness is becoming a rising concern in machine learning. Recent research has discovered that state-of-the-art models are amplifying social bias by making biased prediction towards some population groups (characterized by sensitive features like race or gender). Such unfair prediction among groups renders trust issues and ethical concerns in machine learning, especially for sensitive fields such as employment, criminal justice, and trust score assessment. In this paper, we introduce a new framework to improve machine learning fairness. The goal of our model is to minimize the influence of sensitive feature from the perspectives of both data input and predictive model. To achieve this goal, we reformulate the data input by eliminating the sensitive information and strengthen model fairness by minimizing the marginal contribution of the sensitive feature. We propose to learn the sensitive-irrelevant input via sampling among features and design an adversarial network to minimize the dependence between the reformulated input and the sensitive information. Empirical results validate that our model achieves comparable or better results than related state-of-the-art methods w.r.t. both fairness metrics and prediction performance.