430 citations · 764 across the 14 of their papers we have counts for
9 papers · 1 filter
Temporal Predictive Coding For Model-Based Planning In Latent Space
Tung Nguyen, Rui Shu, Tuan Pham +2
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments. To handle high-dimensional sensory i…
Anytime Sampling for Autoregressive Models via Ordered Autoencoding
Yilun Xu, Yang Song, Sahaj Garg +4
Autoregressive models are widely used for tasks such as image and audio generation. The sampling process of these models, however, does not allow interruptions and cannot adapt to…
Predictive Coding for Locally-Linear Control
Rui Shu, Tung Nguyen, Yinlam Chow +5
High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks. The Learning Controllable Embedding…
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…
Constructing Unrestricted Adversarial Examples with Generative Models
Yang Song, Rui Shu, Nate Kushman +1
Adversarial examples are typically constructed by perturbing an existing data point within a small matrix norm, and current defense methods are focused on guarding against this typ…
Robust Locally-Linear Controllable Embedding
Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh +2
Embed-to-control (E2C) is a model for solving high-dimensional optimal control problems by combining variational auto-encoders with locally-optimal controllers. However, the E2C mo…