Workshop on Explainable AI in Finance
Interpretable Machine Learning for Time-Series Forecasting
In this talk I will present two approaches for making machine learning for time-series forecasting interpretable and actionable.
The first approach uses post-hoc interpretability to turn black-box machine learning into interpretable insights. I will describe here the first methods for feature-based and for example-based interpretability of machine learning for time-series forecasting – Dynamask (ICML 2021) and Simplex (NeurIPS 2021), respectively.
The second approach directly discovers interpretable closed-form ordinary differential equations (ODEs) from data using machine learning. I will describe here the first automated tool to distill closed-form ODEs from observed trajectories, D-CODE (ICLR 2022), which we believe will accelerate the modeling process of dynamical systems in finance, in healthcare, and beyond.
Location and local date/time
This event will take place in person on November 2 at 19:30 GMT.
About the event
This workshop aims to bring together academic researchers, industry practitioners and financial experts to discuss the key opportunities and focus areas within XAI – both in general and to face the unique challenges in the financial sector.