Mixed Effect
Mixed effects modeling investigates how different factors influence an outcome, accounting for both fixed and random effects. Current research focuses on understanding the impact of various factors across diverse applications, employing diverse models such as deep neural networks, logistic regression, and random feature models, often within the context of interpretability and bias mitigation. This field is crucial for advancing understanding in various domains, from improving AI systems and human-computer interaction to enhancing medical diagnoses and optimizing industrial processes.
Papers
On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging
Klaas Dijkstra, Maya Aghaei, Femke Jaarsma, Martin Dijkstra, Rudy Folkersma, Jan Jager, Jaap van de Loosdrecht
Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability
Yoshinari Fujinuma, Jordan Boyd-Graber, Katharina Kann
Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns
Daniel Wiechmann, Yu Qiao, Elma Kerz, Justus Mattern
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness
Tejas Gokhale, Swaroop Mishra, Man Luo, Bhavdeep Singh Sachdeva, Chitta Baral