430 citations · 764 across the 14 of their papers we have counts for
5 papers · 1 filter
Fair Generative Modeling via Weak Supervision
Kristy Choi, Aditya Grover, Trisha Singh +2
Real-world datasets are often biased with respect to key demographic factors such as race and gender. Due to the latent nature of the underlying factors, detecting and mitigating b…
Weakly Supervised Disentanglement with Guarantees
Rui Shu, Yining Chen, Abhishek Kumar +2
Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, con…
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
Nir Levine, Yinlam Chow, Rui Shu +3
Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensional observations and unknown dynamics. A promising approach is to embed t…
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Aditya Grover, Christopher Chute, Rui Shu +2
Given datasets from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain. Variants of this problem have been studied in many…
Training Variational Autoencoders with Buffered Stochastic Variational Inference
Rui Shu, Hung H. Bui, Jay Whang +1
The recognition network in deep latent variable models such as variational autoencoders (VAEs) relies on amortized inference for efficient posterior approximation that can scale up…