By tapping into knowledge stored explicitly in text corpora, retrieval helps tackle the inefficiency, opaqueness, and static nature of large language models.
How can we use machine learning to fix source code errors (e.g. in C, Python) for us? We introduce Break-It-Fix-It, a new unsupervised method to train code repair models.
A retrospective narrative from the Hazy research lab on our work in data-centric AI, and current efforts on engaging the broader machine learning community.
A novel computational tool for policymakers to assess the impacts of thousands of different mobility measures on predicted COVID-19 infections, helping them to navigate difficult tradeoffs between the economy and public health.
We conducted an extensive study of six offline learning algorithms for robot manipulation on five simulated and three real-world multi-stage manipulation tasks of varying complexity, and with datasets of varying quality. Our study analyzes the most critical challenges when learning from offline h...
A meta-learning system for automatic feedback to student code education with applications to university level courses and a large scale online learning platform.
