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62 Results
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Poster
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Mon 21:00 |
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems Kaiqing Zhang · Bin Hu · Tamer Basar |
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Spotlight
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Tue 19:30 |
Large-Scale Adversarial Training for Vision-and-Language Representation Learning Zhe Gan · Yen-Chun Chen · Linjie Li · Chen Zhu · Yu Cheng · Jingjing Liu |
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Poster
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Tue 21:00 |
Large-Scale Adversarial Training for Vision-and-Language Representation Learning Zhe Gan · Yen-Chun Chen · Linjie Li · Chen Zhu · Yu Cheng · Jingjing Liu |
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Poster
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Mon 21:00 |
Adversarial Counterfactual Learning and Evaluation for Recommender System Da Xu · Chuanwei Ruan · Evren Korpeoglu · Sushant Kumar · Kannan Achan |
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Poster
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Tue 9:00 |
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot Jingtong Su · Yihang Chen · Tianle Cai · Tianhao Wu · Ruiqi Gao · Liwei Wang · Jason Lee |
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Poster
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Thu 9:00 |
f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning Xin Zhang · Yanhua Li · Ziming Zhang · Zhi-Li Zhang |
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Poster
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Tue 9:00 |
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics Parameswaran Kamalaruban · Yu-Ting Huang · Ya-Ping Hsieh · Paul Rolland · Cheng Shi · Volkan Cevher |
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Poster
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Thu 9:00 |
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization Paul Barde · Julien Roy · Wonseok Jeon · Joelle Pineau · Chris Pal · Derek Nowrouzezahrai |
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Spotlight
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Thu 7:30 |
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization Paul Barde · Julien Roy · Wonseok Jeon · Joelle Pineau · Chris Pal · Derek Nowrouzezahrai |
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Poster
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Wed 21:00 |
Unsupervised Learning of Object Landmarks via Self-Training Correspondence Dimitrios Mallis · Enrique Sanchez · Matthew Bell · Georgios Tzimiropoulos |
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Poster
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Wed 9:00 |
Why are Adaptive Methods Good for Attention Models? Jingzhao Zhang · Sai Praneeth Karimireddy · Andreas Veit · Seungyeon Kim · Sashank Reddi · Sanjiv Kumar · Suvrit Sra |
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Poster
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Thu 9:00 |
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks Roman Pogodin · Peter E Latham |