Search Results for author:
Found 6 papers, 1 papers with code
M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence Retrieval
In recent research, contrastive learning has proven to be a highly effective method for representation learning and is widely used for dense retrieval.
Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously
The endeavor to preserve the generalization of a fair and invariant classifier across domains, especially in the presence of distribution shifts, becomes a significant and intricate challenge in machine learning.
Towards Fair Disentangled Online Learning for Changing Environments
To this end, in this paper, we propose a novel algorithm under the assumption that data collected at each time can be disentangled with two representations, an environment-invariant semantic factor and an environment-specific variation factor.
Proposing an Interactive Audit Pipeline for Visual Privacy Research
Our goal is to systematically analyze the machine learning pipeline for visual privacy and bias issues.
AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding
However, this approach constrains knowledge sharing across different attributes.
