99 citations · 149 across the 15 of their papers we have counts for
6 papers · 1 filter
Fairness through Aleatoric Uncertainty
Anique Tahir, Lu Cheng, Huan Liu
We propose a simple yet effective solution to tackle the often-competing goals of fairness and utility in classification tasks. While fairness ensures that the model's predictions…
Distributional Shift Adaptation using Domain-Specific Features
Anique Tahir, Lu Cheng, Ruocheng Guo +1
Machine learning algorithms typically assume that the training and test samples come from the same distributions, i.e., in-distribution. However, in open-world scenarios, streaming…
Domain Generalization -- A Causal Perspective
Paras Sheth, Raha Moraffah, K. Selçuk Candan +2
Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary assumptions of these models is the independent and identical distribution, which…
Deep Graph Learning for Anomalous Citation Detection
Jiaying Liu, Feng Xia, Xu Feng +2
Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to…
Evaluation Methods and Measures for Causal Learning Algorithms
Lu Cheng, Ruocheng Guo, Raha Moraffah +3
The convenient access to copious multi-faceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-ba…
Causal Inference for Time series Analysis: Problems, Methods and Evaluation
Raha Moraffah, Paras Sheth, Mansooreh Karami +5
Time series data is a collection of chronological observations which is generated by several domains such as medical and financial fields. Over the years, different tasks such as c…