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Found 12 papers, 4 papers with code
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs
Causal discovery is crucial for causal inference in observational studies: it can enable the identification of valid adjustment sets (VAS) for unbiased effect estimation.
Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment
xOrder consistently achieves a better balance between the algorithm utility and ranking fairness on a variety of datasets with different metrics.
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
While diffusion models excel at generating high-quality samples, their latent variables typically lack semantic meaning and are not suitable for representation learning.
Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources
By offering a more comprehensive approach to healthcare data integration, patchwork learning has the potential to revolutionize the clinical applicability of ML models.
Correcting the User Feedback-Loop Bias for Recommendation Systems
We empirically validated the existence of such user feedback-loop bias in real world recommendation systems and compared the performance of our method with the baseline models that are either without de-biasing or with propensity scores estimated by other methods.
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
In this paper, we propose an FL framework to jointly consider performance consistency and algorithmic fairness across different local clients (data sources).
Collaboration Equilibrium in Federated Learning
Federated learning (FL) refers to the paradigm of learning models over a collaborative research network involving multiple clients without sacrificing privacy.
The Definitions of Interpretability and Learning of Interpretable Models
As machine learning algorithms getting adopted in an ever-increasing number of applications, interpretation has emerged as a crucial desideratum.
Unsupervised Disentanglement Learning by intervention
In this paper, we focus on the unsupervised learning of disentanglement in a general setting which the generative factors may be correlated.
Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility
Bipartite ranking, which aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data, is widely adopted in various applications where sample prioritization is needed.
Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm
Object detection when provided image-level labels instead of instance-level labels (i. e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain.
