Machine Learning Solutions & Services Group

Lin, Hengxu, et al. “Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport (opens in new tab).” Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2021.

Wu, Xueqing, et al. “Temporally Correlated Task Scheduling for Sequence Learning.”  (opens in new tab)International Conference on Machine Learning. PMLR, 2021.

Fang, Yuchen, et al. “Universal Trading for Order Execution with Oracle Policy Distillation (opens in new tab).” arXiv preprint arXiv:2103.10860 (2021).

Xu, Wentao, et al. “REST: Relational Event-driven Stock Trend Forecasting (opens in new tab).” Proceedings of the Web Conference 2021. 2021.

Yang, Xiao, et al. “Qlib: An AI-oriented Quantitative Investment Platform (opens in new tab).” arXiv preprint arXiv:2009.11189 (2020).

Yang, Xiao, et al. “A divide-and-conquer framework for attention-based combination of multiple investment strategies (opens in new tab).” 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2019.

Wang, Lewen, et al. “Conservative or Aggressive? Confidence-Aware Dynamic Portfolio Construction (opens in new tab).” 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2019.

Chen, Chi, et al. “Investment behaviors can tell what inside: Exploring stock intrinsic properties for stock trend prediction (opens in new tab).” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019.

Li, Zhige, et al. “Individualized indicator for all: Stock-wise technical indicator optimization with stock embedding (opens in new tab).” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019.

Ding, Yi, et al. “Investor-imitator: A framework for trading knowledge extraction (opens in new tab).” Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018.

Hu, Ziniu, et al. “Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction (opens in new tab).” Proceedings of the eleventh ACM international conference on web search and data mining. 2018.

Lin, Hengxu, et al. “Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation (opens in new tab).” arXiv preprint arXiv:2107.05201 (2021).

Tang, Hongshun, et al. “ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting (opens in new tab).” arXiv preprint arXiv:2012.06289 (2020).

Chen, Chi, et al. “Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction (opens in new tab).” arXiv preprint arXiv:2002.06878 (2020).

Xing J, Zheng S, Li S, L Huang et al. Mimicking atmospheric photochemical modeling with a deep neural network[J]. Atmospheric Research, 2021: 105919.

Huang L, Liu S, Yang Z, et al. Exploring Deep Learning for Air Pollutant Emission Estimation[J]. Geoscientific Model Development Discussions, 2021: 1-22.