2 citations · 6 across the 12 of their papers we have counts for
6 papers · 1 filter
Towards Fair Disentangled Online Learning for Changing Environments
Chen Zhao, Feng Mi, Xintao Wu +4
In the problem of online learning for changing environments, data are sequentially received one after another over time, and their distribution assumptions may vary frequently. Alt…
Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
Aneesh Komanduri, Yongkai Wu, Feng Chen +1
Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information f…
Fairness-Aware Online Meta-learning
Chen Zhao, Feng Chen, Bhavani Thuraisingham
In contrast to offline working fashions, two research paradigms are devised for online learning: (1) Online Meta Learning (OML) learns good priors over model parameters (or learnin…
Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach
Jinlei Zhang, Feng Chen, Lixing Yang +3
Urban rail transit (URT) system plays a dominating role in many megacities like Beijing and Hong Kong. Due to its important role and complex nature, it is always in great need for…
Fair Meta-Learning For Few-Shot Classification
Chen Zhao, Changbin Li, Jincheng Li +1
Artificial intelligence nowadays plays an increasingly prominent role in our life since decisions that were once made by humans are now delegated to automated systems. A machine le…
Unfairness Discovery and Prevention For Few-Shot Regression
Chen Zhao, Feng Chen
We study fairness in supervised few-shot meta-learning models that are sensitive to discrimination (or bias) in historical data. A machine learning model trained based on biased da…