Page 3 of 4 for Machine Learning Posts - Page 3

Learning from Language Explanations

Language is a crucial way for humans to teach other humans. Can we use language to teach machines?

Learning to Fix Programs from Error Messages

We study how to use machine learning to repair programs from error messages, and introduce a promising approach that leverages program-feedback graphs and self-supervised learning.

Adapting on the Fly to Test Time Distribution Shift

Marvin Zhang

We meta-learn models that can use unlabeled test data to adapt to group distribution shift.

GTI: Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations

Ajay Mandlekar, Danfei Xu

We developed Generalization Through Imitation (GTI) - an algorithm for learning visuomotor control from human demonstrations and generalizing to new long-horizon tasks by leveraging latent compositional structures.

Safety Validation of Black-Box Autonomous Systems

Anthony L. Corso

Validation of safety-critical autonomous systems is necessary before deployment and is made possible by black-box validation strategies. In this blog post, we provide a model for black-box safety validation and survey the literature to uncover the most effective validation techniques.

Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning

Evan Zheran Liu

Adapting to new environments and tasks (e.g., cooking in a new kitchen) requires first gathering information via exploration (e.g., finding the ingredients). We identify a chicken-and-egg coupling problem that prevents existing approaches from effectively exploring, and we solve this with DREAM.

Back to the Future: Planning-Aware Trajectory Forecasting for Autonomous Driving

Boris Ivanovic

Recent advances in deep generative modeling have brought forth a paradigm shift in trajectory forecasting. In this blog post, we provide an overview of existing work, highlight new opportunities, and present our latest work on developing methods that are cognizant of their downstream use cases.

Making Sense of Vision and Touch: Multimodal Representations for Contact-Rich Tasks

Michelle A. Lee

Contact-rich manipulation tasks in unstructured environments often require both tactile and visual feedback. In this blog post, we introduce how to use self-supervision to learn a compact and multimodal representation of vision and touch.

Leveraging Compositionality for One-Shot Imitation Learning

De-An Huang, Danfei Xu

We introduce a class of algorithms that solve long-horizon one-shot imitation learning by leveraging compositional priors.

Automating Data Augmentation: Practice, Theory and New Direction

Sharon Y. Li

Data augmentation is a de facto technique used in nearly every state-of-the-art machine learning model. In this blog post, we provide an overview of recent work on the practice, theory and new direction of data augmentation research.