Search Results for author: Jinning Li

Found 12 papers, 3 papers with code

Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting

1 code implementation20 Oct 2023 Chenkai Sun, Jinning Li, Yi R. Fung, Hou Pong Chan, Tarek Abdelzaher, ChengXiang Zhai, Heng Ji

Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative outcomes such as social conflict and moral injury.

Language Modelling Large Language Model

Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization

no code implementations11 Oct 2023 Yuxin Chen, Chen Tang, Ran Tian, Chenran Li, Jinning Li, Masayoshi Tomizuka, Wei Zhan

We observe that, generally, a more diverse set of co-play agents during training enhances the generalization performance of the ego agent; however, this improvement varies across distinct scenarios and environments.

Multi-agent Reinforcement Learning

Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration

no code implementations18 Sep 2023 Jinning Li, Xinyi Liu, Banghua Zhu, Jiantao Jiao, Masayoshi Tomizuka, Chen Tang, Wei Zhan

GOLD distills an offline DT policy into a lightweight policy network through guided online safe RL training, which outperforms both the offline DT policy and online safe RL algorithms.

Autonomous Driving Decision Making +3

Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning

no code implementations13 Jun 2023 Ruijie Wang, Baoyu Li, Yichen Lu, Dachun Sun, Jinning Li, Yuchen Yan, Shengzhong Liu, Hanghang Tong, Tarek F. Abdelzaher

State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues.

Knowledge Graphs World Knowledge

Measuring the Effect of Influential Messages on Varying Personas

1 code implementation25 May 2023 Chenkai Sun, Jinning Li, Hou Pong Chan, ChengXiang Zhai, Heng Ji

Our analysis shows that the best-performing models are capable of predicting responses that are consistent with the personas, and as a byproduct, the task formulation also enables many interesting applications in the analysis of social network groups and their opinions, such as the discovery of extreme opinion groups.

Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs

no code implementations16 Oct 2022 Ruijie Wang, Zheng Li, Dachun Sun, Shengzhong Liu, Jinning Li, Bing Yin, Tarek Abdelzaher

Second, the potentially dynamic distributions from the initially observable facts to the future facts ask for explicitly modeling the evolving characteristics of new entities.

Knowledge Graphs Meta-Learning

Dealing with the Unknown: Pessimistic Offline Reinforcement Learning

no code implementations9 Nov 2021 Jinning Li, Chen Tang, Masayoshi Tomizuka, Wei Zhan

Reinforcement Learning (RL) has been shown effective in domains where the agent can learn policies by actively interacting with its operating environment.

reinforcement-learning Reinforcement Learning (RL)

Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders

1 code implementation1 Oct 2021 Jinning Li, Huajie Shao, Dachun Sun, Ruijie Wang, Yuchen Yan, Jinyang Li, Shengzhong Liu, Hanghang Tong, Tarek Abdelzaher

Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e. g., posts that represent user views) into an appropriate disentangled latent space.

Representation Learning Stance Detection

Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking

no code implementations18 Feb 2021 Jiachen Li, Hengbo Ma, Zhihao Zhang, Jinning Li, Masayoshi Tomizuka

Due to the existence of frequent interactions and uncertainty in the scene evolution, it is desired for the prediction system to enable relational reasoning on different entities and provide a distribution of future trajectories for each agent.

Autonomous Vehicles Navigate +2

A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning

no code implementations17 Jan 2021 Jinning Li, Liting Sun, Jianyu Chen, Masayoshi Tomizuka, Wei Zhan

To address this challenge, we propose a hierarchical behavior planning framework with a set of low-level safe controllers and a high-level reinforcement learning algorithm (H-CtRL) as a coordinator for the low-level controllers.

Autonomous Vehicles reinforcement-learning +1

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