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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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
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
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
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
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
Towers of Saliency: A Reinforcement Learning Visualization Using Immersive Environments
ACM Interactive Surfaces and Spaces (ISS), 2019
Authors:
N. Douglas,
D. Yim,
B. Kartal,
P. Hernandez-Leal,
M. E. Taylor,
F. Maurer