Jiang, Minqi

How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection

Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., Yue, J., & Wu, Y. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 318

An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms

Jiang, M., Liu, J., Zhang, L., & Liu, C. (2020) · Physica A: Statistical Mechanics and its Applications

Stock Market Forecasting MethodsForecasting Techniques and Applications

Cited by 155

Prioritized Level Replay

Jiang, M., Grefenstette, E., & Rocktäschel, T. (2020) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 33

Evolving Curricula with Regret-Based Environment Design

Parker-Holder, J., Jiang, M., Dennis, M., Samvelyan, M., Foerster, J., Grefenstette, E., & Rocktäschel, T. (2022) · arXiv

Reinforcement Learning in RoboticsEvolutionary Algorithms and Applications

Cited by 28

Replay-Guided Adversarial Environment Design

Jiang, M., Dennis, M., Parker-Holder, J., Foerster, J., Grefenstette, E., & Rocktäschel, T. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 21

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

Samvelyan, M., Kirk, R., Kurin, V., Parker-Holder, J., Jiang, M., Hambro, E., Petroni, F., Küttler, H., Grefenstette, E., & Rocktäschel, T. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 20

General intelligence requires rethinking exploration

Jiang, M., Rocktäschel, T., & Grefenstette, E. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 17

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning

Samvelyan, M., Khan, A., Dennis, M., Jiang, M., Parker-Holder, J., Foerster, J., Raileanu, R., & Rocktäschel, T. (2023) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 8

A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs

Henaff, M., Jiang, M., & Raileanu, R. (2023) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 1

Insights From the NeurIPS 2021 NetHack Challenge

Hambro, E., Mohanty, S., Babaev, D., Byeon, M., Chakraborty, D., Grefenstette, E., Jiang, M., Jo, D., Kanervisto, A., Kim, J., et al. (2022) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1

Stabilizing Unsupervised Environment Design with a Learned Adversary

Mediratta, I., Jiang, M., Parker-Holder, J., Dennis, M., Vinitsky, E., & Rocktäschel, T. (2023) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 1

Refining Minimax Regret for Unsupervised Environment Design

Beukman, M., Coward, S., Matthews, M., Fellows, M., Jiang, M., Dennis, M., & Foerster, J. (2024) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 0

Hyperagents

Zhang, J., Zhao, B., Yang, W., Foerster, J., Clune, J., Jiang, M., Devlin, S., & Shavrina, T. (2026) · arXiv

Evolutionary Algorithms and ApplicationsAI-based Problem Solving and Planning

Cited by 0

APRES: An Agentic Paper Revision and Evaluation System

Zhao, B., Zhang, J., Whitehouse, C., Jiang, M., Shvartsman, M., Charnalia, A., Magka, D., Shavrina, T., Dunfield, D., Mac Aodha, O., et al. (2026) · arXiv

Academic Writing and Publishingscientometrics and bibliometrics research

Cited by 0