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Workshop: AI in Finance: Applications and Infrastructure for Multi-Agent Learning

Poster Highlights - Lightning Round


Self Organizing Supply Chains for Micro-Prediction; Present and Future Uses of the ROAR Protocol, Peter D Cotton (JP Morgan Chase)

Learning-Based Trading Strategies in the Face of Market Manipulation, Xintong Wang (University of Michigan); Chris Hoang (University of Michigan); Michael Wellman (University of Michigan)

Multi-Agent Simulation for Pricing and Hedging in a Dealer Market, Sumitra Ganesh (JPMorgan AI Research); Nelson Vadori (JPMorgan AI Research); Mengda Xu (JPMorgan AI Research); Hua Zheng (JPMorgan Chase); Prashant Reddy (JPMorgan AI Research); Manuela Veloso (JPMorgan AI Research)

Multi-Agent Reinforcement Learning for Liquidation Strategy Analysis, Wenhang Bao (Columbia University); Xiao-Yang Liu (Columbia University)

Some people aren't worth listening to: periodically retraining classifiers with feedback from a team of end users, Joshua Lockhart (JPMorgan AI Research); Mahmoud Mahfouz (JPMorgan AI Research); Tucker Balch (JPMorgan AI Research); Manuela Veloso (JPMorgan AI Research)

Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation, Xinyi Li (Columbia University); Yinchuan Li ( Beijing Institute of Technology); Yuancheng Zhan (University of Science and Technology of China); Xiao-Yang Liu (Columbia University)

How to Evaluate Trading Strategies: Backtesting or Agent-based Simulation?, Tucker Balch (JPMorgan AI Research); David Byrd (Georgia Tech); Mahmoud Mahfouz (JPMorgan AI Research)

Deep Reinforcement Learning for Optimal Trade Execution, Siyu Lin (University of Virginia)

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