I am thrilled to announce that I have successfully defended my PhD Thesis, titled Exploring Different Biases in Generative Models, with the highest distinction—summa cum laude. In this work, I demonstrate how my previous papers align with the general notion of bias in generative models. While my research on Benchmarking the Fairness of Image Upsampling Methods addresses the dataset bias, works such as Marginal Tail-Adaptive Normalizing Flows aim at specifying an inductive bias. Lastly, I introduce the novel concept of generator bias, which is used to perform model-attribution as shown in my recent ICML publication Single-Model Attribution of Generative Models Through Final-Layer Inversion.

You can check out all the details in my thesis, which will be made public soon.