The past few years have seen a surge of interest in reinforcement learning, with breakthrough successes in various domains, but the technology remains brittle, often relying on heavily engineered solutions. Several recent works have demonstrated that reinforcement learning algorithms are sensitive to design choices, making it challenging to effectively apply them in practice, especially on novel problems, and limiting their potential impact. This workshop aims to bring together different communities working on solving these problems, including those in reinforcement learning, meta-learning, AutoML, and language models, with the goal of fostering collaboration and cross-pollination of ideas to advance the field of automated reinforcement learning.

Website: https://autorlworkshop.github.io/

Amazon co-organizer: Vu Nguyen

In-context learning (ICL) is an emerging capability of large-scale models, including large language models (LLMs) like GPT-3, to acquire new capabilities directly from the context of an input example without separate training or fine-tuning, enabling these models to adapt rapidly to new tasks, datasets, and domains. This workshop brings together diverse perspectives on this new paradigm to assess progress, synthesize best practices, and chart open problems. Core topics will include architectural and other inductive biases enabling in-context skill acquisition, and reliable evaluation of ICL in application domains including reinforcement learning, representation learning, and safe and reliable machine learning.

Website: https://iclworkshop.github.io/