27 citations · 35 across the 3 of their papers we have counts for
6 papers
Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization
Sungryull Sohn, Hyunjae Woo, Jongwook Choi +4
We tackle real-world problems with complex structures beyond the pixel-based game or simulator. We formulate it as a few-shot reinforcement learning problem where a task is charact…
Environment Generation for Zero-Shot Compositional Reinforcement Learning
Izzeddin Gur, Natasha Jaques, Yingjie Miao +4
Many real-world problems are compositional - solving them requires completing interdependent sub-tasks, either in series or in parallel, that can be represented as a dependency gra…
Adversarial Environment Generation for Learning to Navigate the Web
Izzeddin Gur, Natasha Jaques, Kevin Malta +3
Learning to autonomously navigate the web is a difficult sequential decision making task. The state and action spaces are large and combinatorial in nature, and websites are dynami…
Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques
Jihyeon Lee, Joseph Z. Xu, Kihyuk Sohn +8
To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which in…
Learning to Navigate the Web
Izzeddin Gur, Ulrich Rueckert, Aleksandra Faust +1
Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent's learning through trial-and-error. For instance, fo…
User Modeling for Task Oriented Dialogues
Izzeddin Gur, Dilek Hakkani-Tur, Gokhan Tur +1
We introduce end-to-end neural network based models for simulating users of task-oriented dialogue systems. User simulation in dialogue systems is crucial from two different perspe…