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

Revision as of 01:38, 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

• [VIVEK] Poincare Embeddings for learning Hierarchical Representations, Maximilian Nickel Douwe Kiela https://arxiv.org/pdf/1705.08039.pdf
• Mimicking Word Embeddings using Subword RNNs, Yuval Pinter, Robert Guthrie and Jacob Eisenstein http://aclweb.org/anthology/D17-1010

Relational Learning and Reasoning

• Adversarial Training for Relation Extraction, Yi Wu, David Bamman and Stuart Russell https://people.eecs.berkeley.edu/~russell/papers/emnlp17-relation.pdf

Reinforcement Learning

• Shallow Updates for Deep Reinforcement Learning, Levine et al., NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=9098
• Imagination-Augmented Agents for Deep Reinforcement Learning Racanière et al., NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=10081
• Robust Imitation of Diverse Behaviors, Wang et al. 2017, https://arxiv.org/pdf/1707.02747.pdf
• Programmable Agents, Denil et al., https://arxiv.org/pdf/1706.06383v1.pdf
• Compatible Reward Inverse Reinforcement Learning Metelli et al., NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=8993
• Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation, Wu et al., NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=10087
• Expected Policy Gradients, Kamil Ciosek, Shimon Whiteson, https://arxiv.org/abs/1706.05374
• Reinforcement Learning with Deep Energy-Based Policies Haarnoja et al, ICML 2017 http://proceedings.mlr.press/v70/haarnoja17a/haarnoja17a.pdf
• Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning, Gu et al., NIPS 2017 https://arxiv.org/abs/1706.00387
• Sequence Level Training with Recurrent Neural Networks https://arxiv.org/pdf/1511.06732.pdf
• Distral: Robust multitask reinforcement learning, Teh et al., NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=9227
• Repeated Inverse Reinforcement Learning, Amin et al., NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=10107
• Hybrid Reward Architecture for Reinforcement Learning, Van Seijen et al., NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=9314
• Cold-Start Reinforcement Learning with Softmax Policy Gradient, Ding and Soirut, NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=9067

Generation

• Adversarially Regularized Autoencoders for Generating Discrete Structures, Zhao et al., https://arxiv.org/pdf/1706.04223.pdf

Dialog

• A Deep Reinforcement Learning Chatbot, Serban et al., https://arxiv.org/pdf/1709.02349.pdf

Learning

• Variance-based Regularization with Convex Objectives. Hongseok Namkoong, John Duchi. https://arxiv.org/abs/1610.02581
• 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

• 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