24 citations · 47 across the 9 of their papers we have counts for
10 papers
Unified Auto-Encoding with Masked Diffusion
Philippe Hansen-Estruch, Sriram Vishwanath, Amy Zhang +1
At the core of both successful generative and self-supervised representation learning models there is a reconstruction objective that incorporates some form of image corruption. Di…
Automated Discovery of Functional Actual Causes in Complex Environments
Caleb Chuck, Sankaran Vaidyanathan, Stephen Giguere +3
Reinforcement learning (RL) algorithms often struggle to learn policies that generalize to novel situations due to issues such as causal confusion, overfitting to irrelevant factor…
Dissecting IoT Device Provisioning Process
Rostand A. K. Fezeu, Timothy J. Salo, Amy Zhang +1
We examine in detail the provisioning process used by many common, consumer-grade Internet of Things (IoT) devices. We find that this provisioning process involves the IoT device,…
-Policy Gradients: A General Framework for Goal Conditioned RL using -Divergences
Siddhant Agarwal, Ishan Durugkar, Peter Stone +1
Goal-Conditioned Reinforcement Learning (RL) problems often have access to sparse rewards where the agent receives a reward signal only when it has achieved the goal, making policy…
Motif: Intrinsic Motivation from Artificial Intelligence Feedback
Martin Klissarov, Pierluca D'Oro, Shagun Sodhani +5
Exploring rich environments and evaluating one's actions without prior knowledge is immensely challenging. In this paper, we propose Motif, a general method to interface such prior…
Generalization Across Observation Shifts in Reinforcement Learning
Anuj Mahajan, Amy Zhang
Learning policies which are robust to changes in the environment are critical for real world deployment of Reinforcement Learning agents. They are also necessary for achieving good…