South America
Fairness-Aware Meta-Learning via Nash Bargaining Yi Zeng 1, Xuelin Y ang 2, Li Chen
To address issues of group-level fairness in machine learning, it is natural to adjust model parameters based on specific fairness objectives over a sensitive-attributed validation set.
Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions
's matching capacity will be reduced
Rethinking Human Evaluation Protocol for Text-to-Video Models: Enhancing Reliability, Reproducibility, and Practicality
Despite these strides, evaluating these models poses substantial challenges.
Supplementary Material: T orchSpatial-A Location Encoding Framework and Benchmark for Spatial Representation Learning
Author ordering is determined by coin flip. For what purpose was the dataset created? Was there a specific task in mind? In order to systematically compare the location encoders' performance and their impact on the Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset? Dr. Gengchen Mai acknowledges the Microsoft Research What do the instances that comprise the dataset represent (e.g., documents, photos, people, The instances in all 17 datasets represent images.
