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 research is made freely available to support the AI community.

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Tails of Lipschitz Triangular Flows
International Conference on Machine Learning (ICML), 2020
Authors: P. Jaini, I. Kobyzev, Y. Yu, M. Brubaker
Abstract Paper BibTeX Code
Unsupervised Multilingual Alignment using Wasserstein Barycenter
International Joint Conference on Artificial Intelligence (IJCAI), 2020
Authors: X. Lian, K. Jain, J. Truszkowski, P. Poupart, Y. Yu
Abstract Paper BibTeX
Learning User Representations for Open Vocabulary Image Hashtag Prediction
Conference on Computer Vision and Pattern Recognition (CVPR), 2020
Authors: T. Durand
Abstract Paper BibTeX
Normalizing Flows: An Introduction and Review of Current Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE), 2020
Authors: I. Kobyzev, S. Prince, M. Brubaker
Abstract Paper BibTeX
On Minimax Optimality of GANs for Robust Mean Estimation
Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Authors: G. W. Ding, R. Huang, Y. Yu, K. Wu
Abstract Paper BibTeX
Better Long-Range Dependency by Bootstrapping A Mutual Information Regularizer
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Authors: *Y. Cao, *P. Xu
* Denotes equal contribution
Abstract Paper BibTeX
Temporally Extended Auxiliary Tasks
Workshop on Adaptive and Learning Agents (AAMAS), 2020
Authors: C. Sherstan, B. Kartal, P. Hernandez-Leal, M. E. Taylor
Abstract Paper BibTeX
Multi Type Mean Field Reinforcement Learning
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2020
Authors: S. Subramanian, P. Poupart, M. E. Taylor, N. Hegde
Abstract Paper BibTeX
Max-Margin Adversarial Training: Direct Input Space Margin Maximization through Adversarial Training
International Conference on Learning Representations (ICLR), 2020
Authors: G. W. Ding, Y. Sharma, K. Lui, R. Huang
Abstract Paper BibTeX
Representation Learning for Dynamic Graphs: A Survey
Journal of Machine Learning Research (JMLR), 2020
Authors: M. Kazemi, R. Goel, K. Jain, I. Kobyzev, A. Sethi, P. Forsyth, P. Poupart
Abstract Paper BibTeX
Adapting Grad-CAM for Embedding Networks
IEEE Winter Conference on Applications of Computer Vision (WACV), 2020
Authors: L. Chen, J. Chen, H. Hajimirsadeghi, G. Mori
Abstract Paper