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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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
Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents
Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020
Authors: F. L. Da Silva, P. Hernandez-Leal , B. Kartal, M. E. Taylor
Abstract Paper BibTeX
Diachronic Embedding for Temporal Knowledge Graph Completion
Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020
Authors: R. Goel, S. M. Kazemi , M. Brubaker, P. Poupart
Abstract Paper BibTeX Blog
Point Process Flows
Workshop on Learning with Temporal Point Processes (NeurIPS), 2019
Authors: *N. Mehrasa, *R. Deng, M. O. Ahmed , B. Chang, J. He , T. Durand , M. Brubaker, G. Mori
* Denotes equal contribution
Abstract Paper BibTeX
Variational Selective Autoencoder
2nd Symposium on Advances in Approximate Bayesian Inference (AABI), 2019
Authors: *Y. Gong, H. Hajimirsadeghi , *J. He , *M. Nawhal, T. Durand , *G. Mori
* Denotes equal contribution
Abstract Paper BibTeX
Maximum Entropy Monte-Carlo Planning
Neural Information Processing Systems (NeurIPS), 2019
Authors: C. Xiao, R. Huang, J. Mei, D. Schuurmans, M. Müller
Abstract Paper BibTeX
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces
Neural Information Processing Systems (NeurIPS), 2019
Authors: B. Wang, N. Hegde
Abstract Paper BibTeX Blog
Arbitrarily-conditioned Data Imputation
2nd Symposium on Advances in Approximate Bayesian Inference (AABI), 2019
Authors: M. Carvalho, T. Durand , J. He , N. Mehrasa, G. Mori
Abstract Paper BibTeX