Research to help build a better financial future

Our researchers undertake fundamental and applied research and use state-of-the-art ML to address some of the biggest challenges facing the financial services industry today and in the future.

Our widely published research covers a broad range of topics including Reinforcement learning, Natural Language Processing and Time Series Modeling. Our resesarch is made freely available to support the AI community.

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Adversarial Contrastive Estimation
Association for Computational Linguistics (ACL), 2018
Authors: *A. J. Bose, *H. Ling, *Y. Cao
* Denotes equal contribution
Abstract Paper BibTeX Blog
Improving Reinforcement Learning with Human Input
International Joint Conference on Artificial Intelligence (IJCAI), 2018
Authors: M. E. Taylor
Abstract Paper BibTeX Blog
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
International Conference on Learning Representations (ICLR), 2018
Authors: Y. Cao , G. W. Ding , K. Lui , R. Huang
Abstract Paper BibTeX Blog
Min-Max Propagation
Neural Information Processing Systems (NIPS), 2017
Authors: C. Srinivasa , I. Givoni, S. Ravanbahksh, B. J. Frey
Abstract Paper BibTeX
Implicit Manifold Learning on Generative Adversarial Networks
Workshop on Implicit Models (ICML), 2017
Authors: K. Lui , Y. Cao , M. Gazeau, K. S. Zhang
Abstract Paper BibTeX Blog
Automatic Selection of t-SNE Perplexity
Workshop on AutoML (ICML), 2017
Authors: Y. Cao , L. Wang
Abstract Paper BibTeX Blog