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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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
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
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces
Neural Information Processing Systems (NeurIPS), 2019
Authors:
B. Wang,
N. Hegde
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
Maximum Entropy Monte-Carlo Planning
Neural Information Processing Systems (NeurIPS), 2019
Authors:
C. Xiao,
R. Huang,
J. Mei,
D. Schuurmans,
M. Müller
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
Noise Flow: Noise Modeling with Conditional Normalizing Flows
International Conference on Computer Vision (ICCV), 2019
Authors:
A. Abdelhamed,
M. Brubaker
,
M. S. Brown