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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PUMA: Performance Unchanged Model Augmentation for Training Data Removal
Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022
Authors:
G. Wu
,
M. Hashemi
,
C. Srinivasa
Stay Positive: Knowledge Graph Embedding Without Negative Sampling
Workshop at the International Conference on Machine Learning (ICML), 2020
Authors:
A. Hajimoradlou,
S. M. Kazemi
Continuous Latent Process Flows
Conference on Neural Information Processing Systems (NeurIPS), 2021
Authors:
R. Deng,
M. Brubaker,
G. Mori,
A. Lehrmann
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
Conference on Neural Information Processing Systems (NeurIPS), 2021
Authors:
B. Fatemi,
L. El Asri,
M. Kazemi
Not Too Close and Not Too Far: Enforcing Monotonicity Requires Penalizing The Right Points
Workshop at Conference on Neural Information Processing Systems (NeurIPS), 2021
Authors:
J. Monteiro,
M. O. Ahmed,
H. Hajimirsadeghi,
G. Mori
Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets
Workshop at the International Conference on Machine Learning (ICML), 2021
Authors:
*Y. Gao,
K. Y. C. Lui,
P. Hernandez-Leal
* Denotes equal contribution
Heterogeneous Multi-task Learning with Expert Diversity
Workshop at SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021
Authors:
R. Aoki,
F. Tung,
G. Oliveira
A Globally Normalized Neural Model for Semantic Parsing
Workshop at Association for Computational Linguistics (ACL), 2021
Authors:
C. Huang,
W. Yang,
Y. Cao,
O. R. Zaïane,
L. Mou