Invited & Contributed Talks

2022

Ecological Theory of Reinforcement Learning: How Does Task Design Influence Agent Learning? Workshop at NeurIPS 2021. Structural Assumptions for Better Generalization in Reinforcement Learning. (Invited)
Nvidia, UCLA, UNC Chapel Hill, Yale, Princeton, CMU, UT Austin, USC.
UPenn Department of Computer and Information Science’s Spring Colloquia Series. (Invited)
Intelligent Robot Motion Lab, Princeton University. Exploiting latent structure and bisimulation for better generalization. (Invited)
University of Oxford. Exploiting latent structure and bisimulation for better generalization. Video. (Invited)

Computational Intelligence, Learning, Vision, and Robotics Lab at NYU. Exploiting latent structure and bisimulation for better generalization. (Invited)
Deep Reinforcement Learning Workshop, Theory of Reinforcement Learning Program, Simons Institute. Exploiting latent structure and bisimulation for better generalization. (Invited)
NASA Frontier Development Lab. Invariant Causal Prediction for Block MDPs and Deep Bisimulation for Control. (Invited)
Approximately Correct Machine Intelligence Lab at Carnegie Mellon University. Learning Invariant Representations for Reinforcement Learning without Reconstruction. (Invited)
Reinforcement Learning & Adaptive Behavior Group at Brown University. Invariant Causal Prediction for Block MDPs. (Invited)

The Multi-disciplinary Conference on Reinforcement Learning and Decision Making. Learning Causal State Representations of Partially Observable Environments. (Contributed)
The Data Institute Conference. Deep Model-based Reinforcement Learning for Control. (Invited)

International Conference on Machine Learning. Composable Planning with Attributes. (Contributed)
Berkeley Artificial Intelligence Research Lab. Model-based Methods for Planning and Transfer. (Invited)

Women in Machine Learning Workshop. Using Convolutional Neural Networks to Estimate Population Density from High Resolution Satellite Images. (Contributed)