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Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data


Mar 07, 2021
Ran Tian, Masayoshi Tomizuka, Liting Sun


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Local Additivity Based Data Augmentation for Semi-supervised NER


Oct 04, 2020
Jiaao Chen, Zhenghui Wang, Ran Tian, Zichao Yang, Diyi Yang

* EMNLP 2020 

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Bounded Risk-Sensitive Markov Game and Its Inverse Reward Learning Problem


Sep 05, 2020
Ran Tian, Liting Sun, Masayoshi Tomizuka


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Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation


Nov 15, 2019
Ran Tian, Shashi Narayan, Thibault Sellam, Ankur P. Parikh


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Beating humans in a penny-matching game by leveraging cognitive hierarchy theory and Bayesian learning


Oct 22, 2019
Ran Tian, Nan Li, Ilya Kolmanovsky, Anouck Girard

* IEEE 2020 American Control Conference 

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Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation


Oct 20, 2019
Ran Tian, Nan Li, Ilya Kolmanovsky, Yildiray Yildiz, Anouck Girard

* IEEE Intelligent Transportation Systems Transactions 

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Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts


Oct 01, 2018
Ran Tian, Sisi Li, Nan Li, Ilya Kolmanovsky, Anouck Girard, Yildiray Yildiz

* 2018 IEEE Conference on Decision and Control (CDC) 

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Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder


May 24, 2018
Ryo Takahashi, Ran Tian, Kentaro Inui

* Equal contribution from first two authors. Accepted for publication in the ACL 2018 

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The Mechanism of Additive Composition


Mar 07, 2017
Ran Tian, Naoaki Okazaki, Kentaro Inui

* More explanations on theory and additional experiments added. Accepted by Machine Learning Journal 

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Learning Semantically and Additively Compositional Distributional Representations


Jun 08, 2016
Ran Tian, Naoaki Okazaki, Kentaro Inui

* to appear in ACL2016 

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