Deep RL Meets Structured Prediction


Deep Reinforcement Learning Meets Structured Prediction

ICLR 2019 Workshop - May 6th 2019, New Orleans (USA)

Deep Reinforcement Learning (RL) has achieved successes on numerous tasks such as computer games, the game of Go, robotics, etc. Structured prediction aims at modeling highly dependent variables, which applies to a wide range of domains such as natural language processing, computer vision, computational biology, etc. In many cases, structured prediction can be viewed as a sequential decision making process, so a natural question is can we leverage the advances in deep RL to improve structured prediction?

This workshop will bring together experts in structured predictions and reinforcement learning. Specifically, it will provide an overview of existing approaches from various domains to distill generally applicable principles from their successes. We will also discuss the main challenges arising in this setting and outline potential directions for future progress. The target audience consists of researchers and practitioners in these areas. They include, but are not limited to, deep RL for:

  • dialogue

  • semantic parsing

  • program synthesis

  • architecture search

  • machine translation

  • summarization

  • image caption

  • knowledge graph reasoning

  • information extraction

Accepted papers

Connecting the Dots Between MLE and RL for Sequence Generation

Bowen Tan*, Zhiting Hu*, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing

Buy 4 REINFORCE Samples, Get a Baseline for Free!

Wouter Kool, Herke van Hoof, Max Welling

Learning proposals for sequential importance samplers using reinforced variational inference

Zafarali Ahmed, Arjun Karuvally, Doina Precup, Simon Gravel

Learning Neurosymbolic Generative Models via Program Synthesis

Halley Young, Osbert Bastani, Mayur Naik

Multi-agent query reformulation: Challenges and the role of diversity

Rodrigo Nogueira, Jannis Bulian, Massimiliano Ciaramita

A Study of State Aliasing in Structured Prediction with RNNs

Layla El Asri, Adam Trischler

Neural Program Planner for Structured Predictions

Jacob Biloki, Chen Liang, Ni Lao

Robust Reinforcement Learning for Autonomous Driving

Yesmina Jaafra, Jean Luc Laurent, Aline Deruyver, Mohamed Saber Naceur