Poster Session
in
Workshop: Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy

Posters and Open Discussions (see below for poster titles)

Ramya Malur Srinivasan ⋅ Miguel Perez ⋅ Yuanyuan Liu ⋅ Ben Wood ⋅ Dan Philps ⋅ Kyle Brown ⋅ Daniel Martin ⋅ Mykola Pechenizkiy ⋅ Luca Costabello ⋅ Rongguang Wang ⋅ Suproteem Sarkar ⋅ Sangwoong Yoon ⋅ Zhuoran Xiong ⋅ Enguerrand Horel ⋅ Zhu (Drew) Zhang ⋅ Ulf Johansson ⋅ Jonathan Kochems ⋅ Gregory Sidier ⋅ Prashant Reddy ⋅ Lana Cuthbertson ⋅ Yvonne Wambui ⋅ Christelle Marfaing ⋅ Galen Harrison ⋅ Irene Unceta Mendieta ⋅ Thomas Kehler ⋅ Mark Weber ⋅ Li Ling ⋅ Ceena Modarres ⋅ Abhinav Dhall ⋅ Arash Nourian ⋅ David Byrd ⋅ Ajay Chander ⋅ Xiao-Yang Liu ⋅ Hongyang Yang ⋅ Shuang (Sophie) Zhai ⋅ Freddy Lecue ⋅ Sirui Yao ⋅ Rory McGrath ⋅ Artur Garcez ⋅ Vangelis Bacoyannis ⋅ Alexandre Garcia ⋅ Lukas Gonon ⋅ Mark Ibrahim ⋅ Melissa Louie ⋅ Omid Ardakanian ⋅ Cecilia Sönströd ⋅ Kojin Oshiba ⋅ Chaofan Chen ⋅ Suchen Jin ⋅ aldo pareja ⋅ Toyo Suzumura

2018 Poster Session
in
Workshop: Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy

Abstract

Clustering and Learning from Imbalanced Data
Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning
Generating User-friendly Explanations for Loan Denials using GANs
Practical Deep Reinforcement Learning Approach for Stock Trading
Idiosyncrasies and challenges of data driven learning in electronic trading
Machine learning-aided modeling of fixed income instruments
An Interpretable Model with Globally Consistent Explanations for Credit Risk
Continuous learning augmented investment decisions
HELOC Applicant Risk Performance Evaluation by Topological Hierarchical Decomposition
Looking Deeper into the Deep Learning Models: Attribution-based Explanations of TextCNN
Matrix Regression and Its Applications in Cryptocurrency Trading
Sensitivity based Neural Networks Explanations
On the Need for Fairness in Financial Recommendation Engines
Read the News, not the Books: Predicting Firms’ Financial Health

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