Difference between revisions of "Winter 2018 CS595I Advanced NLP/ML Seminar"
From courses
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* 01/27 | * 01/27 | ||
** [Mahnaz] Sequence Level Training with Recurrent Neural Networks, Ranzato et al., https://arxiv.org/abs/1511.06732 | ** [Mahnaz] Sequence Level Training with Recurrent Neural Networks, Ranzato et al., https://arxiv.org/abs/1511.06732 | ||
| + | ** [Zimu] Programmable Agents, Denil et al., https://arxiv.org/pdf/1706.06383v1.pdf | ||
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* Imagination-Augmented Agents for Deep Reinforcement Learning Racanière et al., NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=10081 | * 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 | * Robust Imitation of Diverse Behaviors, Wang et al. 2017, https://arxiv.org/pdf/1707.02747.pdf | ||
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* Compatible Reward Inverse Reinforcement Learning Metelli et al., NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=8993 | * 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 | * 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 | ||
Revision as of 21:44, 22 January 2018
Time: Monday 5-6pm, starting 01/22. Location: HFH 1132.
If you registered this class, you should contact the instructor to present one paper *and* be the discussant of one paper below.
- Presenter: prepare a short summary of no more than 15 mins of presentation.
- Discussant: by presenting a paper in one session, you automatically become the discussant of the other paper. Please 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.
- 01/22
- [VIVEK] Poincare Embeddings for learning Hierarchical Representations, Maximilian Nickel Douwe Kiela https://arxiv.org/pdf/1705.08039.pdf
- [Wenhan] One-shot imitation learning, Duan et al., http://papers.nips.cc/paper/6709-one-shot-imitation-learning
- 01/27
- [Mahnaz] Sequence Level Training with Recurrent Neural Networks, Ranzato et al., https://arxiv.org/abs/1511.06732
- [Zimu] Programmable Agents, Denil et al., https://arxiv.org/pdf/1706.06383v1.pdf
- 02/05
- [Sanjana] Mimicking Word Embeddings using Subword RNNs, Yuval Pinter, Robert Guthrie and Jacob Eisenstein http://aclweb.org/anthology/D17-1010
Contents
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
- Counterfactual Multi−Agent Policy Gradients", Foerster et al., AAAI 2018, Outstanding Student Paper, http://www.cs.ox.ac.uk/people/shimon.whiteson/pubs/foersteraaai18.pdf
- 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
- 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
