Search Results for author: Christan Grant

Found 6 papers, 1 papers with code

M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence Retrieval

no code implementations21 Mar 2024 Yang Bai, Anthony Colas, Christan Grant, Daisy Zhe Wang

In recent research, contrastive learning has proven to be a highly effective method for representation learning and is widely used for dense retrieval.

Contrastive Learning Fact Verification +5

Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously

1 code implementation23 Nov 2023 Chen Zhao, Kai Jiang, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Grant, Feng Chen

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.

Domain Generalization Fairness +1

Towards Fair Disentangled Online Learning for Changing Environments

no code implementations31 May 2023 Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Christan Grant, Feng Chen

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.


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