Poster
in
Workshop: Machine Learning for Systems
Choice-Based Learning in JAX
Daniel Zheng · Shangyin Tan · Gordon Plotkin · Ningning Xie
Abstract:
Choice-based learning is a programming paradigm for expressing learning system in terms of choices and losses. We explore a practical implementation of choice-based learning in JAX by combining two techniques in a novel way: algebraic effects and the selection monad. We describe the design and implementation of our library, explore its usefulness for real-world applications like hyperparameter tuning and deep reinforcement learning, and compare it with existing approaches.
Chat is not available.