16 citations · 65 across the 48 of their papers we have counts for
14 papers · 1 filter
LLM-Based World Models Can Make Decisions Solely, But Rigorous Evaluations are Needed
Chang Yang, Xinrun Wang, Junzhe Jiang +2
World model emerges as a key module in decision making, where MuZero and Dreamer achieve remarkable successes in complex tasks. Recent work leverages Large Language Models (LLMs) a…
CLR-Bench: Evaluating Large Language Models in College-level Reasoning
Junnan Dong, Zijin Hong, Yuanchen Bei +3
Large language models (LLMs) have demonstrated their remarkable performance across various language understanding tasks. While emerging benchmarks have been proposed to evaluate LL…
Double Oracle Neural Architecture Search for Game Theoretic Deep Learning Models
Aye Phyu Phyu Aung, Xinrun Wang, Ruiyu Wang +4
In this paper, we propose a new approach to train deep learning models using game theory concepts including Generative Adversarial Networks (GANs) and Adversarial Training (AT) whe…
Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection
Yewen Li, Chaojie Wang, Xiaobo Xia +6
Unsupervised out-of-distribution (U-OOD) detection is to identify OOD data samples with a detector trained solely on unlabeled in-distribution (ID) data. The likelihood function es…
In-Context Exploiter for Extensive-Form Games
Shuxin Li, Chang Yang, Youzhi Zhang +5
Nash equilibrium (NE) is a widely adopted solution concept in game theory due to its stability property. However, we observe that the NE strategy might not always yield the best re…
MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading
Chuqiao Zong, Chaojie Wang, Molei Qin +3
High-frequency trading (HFT) that executes algorithmic trading in short time scales, has recently occupied the majority of cryptocurrency market. Besides traditional quantitative t…