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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Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning
AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 2019
Authors:
*B. Kartal,
*P. Hernandez-Leal
,
M. E. Taylor
* Denotes equal contribution
On the Effectiveness of Low Frequency Perturbations
International Joint Conference on Artificial Intelligence (IJCAI), 2019
Authors:
Y. Sharma,
G. W. Ding
,
M. Brubaker
On Principled Entropy Exploration in Policy Optimization
International Joint Conference on Artificial Intelligence (IJCAI), 2019
Authors:
*J. Mei,
*C. Xiao,
R. Huang,
D. Schuurmans,
M. Müller
* Denotes equal contribution
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition
The Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019
Authors:
C. Gao,
P. Hernandez-Leal
,
B. Kartal,
M. E. Taylor
Metatrace Actor-Critic: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning
International Joint Conference on Artificial Intelligence (IJCAI), 2019
Authors:
K. Young,
B. Wang,
M. E. Taylor
Learning a Deep ConvNet for Multi-label Classification with Partial Labels
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Uniform Stability and High Order Approximation of SGLD in Non-Convex Learning
Workshop on Understanding and Improving Generalization in Deep Learning (ICML), 2019
Authors:
*M. Gazeau,
*M. Li
* Denotes equal contribution
A Survey and Critique of Multiagent Deep Reinforcement Learning
Journal of Autonomous Agents and Multiagent Systems (JAAMAS), 2019
Authors:
P. Hernandez-Leal
,
B. Kartal,
M. E. Taylor
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman
Workshop on Adaptive Learning Agents (AAMAS), 2019
Authors:
B. Kartal,
P. Hernandez-Leal
,
C. Gao,
M. E. Taylor