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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Generating Videos of Zero-Shot Compositions of Actions and Objects
European Conference on Computer Vision (ECCV), 2020
Authors: M. Nawhal, M. Zhai, A. Lehrmann , L. Sigal , G. Mori
Abstract Paper BibTeX
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Workshop on Invertible Neural Networks, Normalizing Flows and Explicit Likelihood Models(ICML), 2020
Authors: R. Deng, B. Chang, M. Brubaker, G. Mori , A. Lehrmann
Abstract Paper BibTeX
On Variational Learning of Controllable Representations for Text without Supervision
International Conference on Machine Learning (ICML), 2020
Authors: P. Xu , Y. Cao
Abstract Paper BibTeX
Evaluating Lossy Compression Rates of Deep Generative Models
International Conference on Machine Learning (ICML), 2020
Authors: *S. Huang, *A. Makhzani, Y. Cao, R. Grosse
* Denotes equal contribution
Abstract Paper BibTeX
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