Difference between revisions of "Winter 2018 CS595I Advanced NLP/ML Seminar"

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===Dialog===
 
===Dialog===
 
* A Deep Reinforcement Learning Chatbot, Serban et al., https://arxiv.org/pdf/1709.02349.pdf
 
* A Deep Reinforcement Learning Chatbot, Serban et al., https://arxiv.org/pdf/1709.02349.pdf
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===Learning===
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* Variance-based Regularization with Convex Objectives. Hongseok Namkoong, John Duchi. https://arxiv.org/abs/1610.02581
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* Safe and Nested Subgame Solving for Imperfect-Information Games. Noam Brown, Tuomas Sandholm. https://nips.cc/Conferences/2017/Schedule?showEvent=8864
  
 
===NLP for Computational Social Science===
 
===NLP for Computational Social Science===
 
*Analyzing Language in Fake News and Political Fact-Checking, Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova and Yejin Choi https://www.cs.jhu.edu/~svitlana/papers/RCYVC_EMNLP2017.pdf
 
*Analyzing Language in Fake News and Political Fact-Checking, Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova and Yejin Choi https://www.cs.jhu.edu/~svitlana/papers/RCYVC_EMNLP2017.pdf
 
*Human Centered NLP with User Factor Adaptation. Veronica Lynn, Youngseo Son, Vivek Kulkarni, Niranjan Balasubramanian, H Andrew Schwartz, http://www.aclweb.org/anthology/D17-1120
 
*Human Centered NLP with User Factor Adaptation. Veronica Lynn, Youngseo Son, Vivek Kulkarni, Niranjan Balasubramanian, H Andrew Schwartz, http://www.aclweb.org/anthology/D17-1120

Revision as of 00:21, 4 January 2018

Time: TBD Location: HFH 1132.

If you registered this class, you should contact the instructor to present one paper *and* be the discussant of two papers below.

  • Presenter: prepare a short summary of no more than 15 mins of presentation.
  • Discussant: prepare two questions for discussion about the paper.

If you don't present or lead the discussion, you will then need to write a 2-page final report in ICML 2018 style, comparing any two of the papers below. Due: TBD to william@cs.ucsb.edu.

Word Embeddings

Relational Learning and Reasoning

Reinforcement Learning

Generation

Dialog

Learning

NLP for Computational Social Science