17 citations · 83 across the 22 of their papers we have counts for
5 papers · 1 filter
Enhancing Counterfactual Classification via Self-Training
Ruijiang Gao, Max Biggs, Wei Sun +1
Unlike traditional supervised learning, in many settings only partial feedback is available. We may only observe outcomes for the chosen actions, but not the counterfactual outcome…
AE-StyleGAN: Improved Training of Style-Based Auto-Encoders
Ligong Han, Sri Harsha Musunuri, Martin Renqiang Min +3
StyleGANs have shown impressive results on data generation and manipulation in recent years, thanks to its disentangled style latent space. A lot of efforts have been made in inver…
Dual Projection Generative Adversarial Networks for Conditional Image Generation
Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos +4
Conditional Generative Adversarial Networks (cGANs) extend the standard unconditional GAN framework to learning joint data-label distributions from samples, and have been establish…
Human-AI Collaboration with Bandit Feedback
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga +3
Human-machine complementarity is important when neither the algorithm nor the human yield dominant performance across all instances in a given domain. Most research on algorithmic…
Disentangled Recurrent Wasserstein Autoencoder
Jun Han, Martin Renqiang Min, Ligong Han +2
Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as i…