ICML 2019 Workshop on AI in Finance:
Applications and Infrastructure for Multi-Agent Learning
Long Beach, CA, USA
June 14, 2019
Finance is a rich domain for AI and ML research. Model-driven strategies for stock trading and risk assessment models for loan approvals are quintessential financial applications that are reasonably well-understood. However, there are a number of other applications that call for attention as well.
In particular, many finance domains involve ecosystems of interacting and competing agents. Consider for instance the detection of financial fraud and money-laundering. This is a challenging multi-agent learning problem, especially because the real world agents involved evolve their strategies constantly. Similarly, in algorithmic trading of stocks, commodities, etc., the actions of any given trading agent affects, and is affected by, other trading agents -- many of these agents are constantly learning in order to adapt to evolving market scenarios. Further, such trading agents operate at such a speed and scale that they must be fully autonomous. They have grown in sophistication to employ advanced ML strategies including deep learning, reinforcement learning, and transfer learning.
Financial institutions have a long history of investing in technology as a differentiator and have been key drivers in advancing computing infrastructure (e.g., low-latency networking). As more financial applications employ deep learning and reinforcement learning, there is consensus now on the need for more advanced computing architectures--for training large machine learning models and simulating large multi-agent learning systems--that balance scale with the stringent privacy requirements of finance.
Historically, financial firms have been highly secretive about their proprietary technology developments. But now, there is also emerging consensus on the need for (1) deeper engagement with academia to advance a shared knowledge of the unique challenges faced in FinTech, and (2) more open collaboration with academic and technology partners through intellectually sophisticated fora such as this proposed workshop.
Call for Papers
We invite short papers in the following areas:
- Multi-agent systems in finance
- Natural and artificial multi-agent ecosystems with applications to finance
- Multi-agent systems as economic systems
- Infrastructure to support research in multi-agent and financial systems
- Implications of ML on financial markets and other multi-agent systems
- Implications of autonomous trading agents on financial markets
- Simulation of multi-agent and financial systems
We also invite tutorials and introductory papers to bridge the gap between academia and the financial industry:
Overview of Industry Challenges
- Short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers. These papers should describe problems that can inspire new research directions in academia, and should serve to bridge the information gap between academia and the financial industry.
- Short tutorials from academic researchers that explain current solutions to challenges related to multi-agent learning, not necessarily limited to the financial domain. These tutorials will serve as an introduction and enable financial industry practitioners to employ/adapt latest academic research to their use-cases.
All submissions must be PDFs formatted in the ICML style. Submissions are limited to 8 content pages or less, including all figures and tables but excluding references. All accepted papers will be presented as posters; some may be selected for oral presentations, depending on schedule constraints. Accepted papers will be posted on the workshop website or, at the authors’ request, may be linked to an external repository such as arXiv.
Papers should be submitted on CMT3 by Apr 30, 2019 9:00 PM Pacific Time
- Submission deadline: Apr 30, 2019 9:00 PM Pacific Time at https://cmt3.research.microsoft.com/MALICML2019
- Author notification: Week of May 6-10, 2019
- Workshop: Friday June 14, 2019