# Stanford AI Lab Papers and Talks at ICLR 2022

DevFeed: [Stanford AI Lab Papers and Talks at ICLR 2022](<https://devfeed.tech/articles/stanford-ai-lab-papers-and-talks-at-iclr-2022-7584.md>)

Original publisher: [Read original article](<https://ai.stanford.edu/blog/iclr-2022/>)

Author: Compiled by Drew A. Hudson

Published: 2022-04-25T07:00:00Z

Content type: article

Language: en

Sources: [The Stanford AI Lab Blog](<https://devfeed.tech/sources/the-stanford-ai-lab-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [Robotics](<https://devfeed.tech/topics/robotics.md>), [Robotic manipulation](<https://devfeed.tech/topics/robotic-manipulation.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [iclr](<https://devfeed.tech/tags/iclr.md>), [knowledge-graph](<https://devfeed.tech/tags/knowledge-graph.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [robotic-manipulation](<https://devfeed.tech/tags/robotic-manipulation.md>), [robotics](<https://devfeed.tech/tags/robotics.md>)

## AI overview

Stanford AI Lab presents its work at ICLR 2022, including papers and talks on reinforcement learning, benchmark datasets, distribution shifts, in-context learning, language models, graph reasoning, model editing, robotics, and robotic manipulation.

## Source excerpt

The International Conference on Learning Representations (ICLR) 2022 is being hosted virtually from April 25th - April 29th. We're excited to share all the work from SAIL that's being presented, and you'll find links to papers, videos and blogs below. Feel free to reach out to the contact authors directly to learn more about the work that's happening at Stanford! List of Accepted Papers Autonomous Reinforcement Learning: Formalism and Benchmarking Authors: Archit Sharma*, Kelvin Xu*, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn Contact: architsh@stanford.edu Links: Paper | Website Keywords: reinforcement learning, continual learning, reset-free reinforcement learning MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts Authors: Weixin Liang, James Zou Contact: wxliang@stanford.edu Links: Paper | Video | Website Keywords: benchmark dataset, distribution shift, out-of-domain generalization An Explanation of In-context Learning as Implicit Bayesian Inference Authors: Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma Contact: xie@cs.stanford.edu Links: Paper | Video Keywords: gpt-3, in-context learning, pretraining, few-shot learning GreaseLM: Graph REASoning Enhanced Language Models for Question Answering Authors: Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher D. Manning, Jure Leskovec Contact: xikunz2@cs.stanford.edu Award nominations: Spotlight Links: Paper | Website Keywords: knowledge graph, question answering, language model, commonsense reasoning, graph neural networks, biomedical qa Fast Model Editing at Scale Authors: Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D. Manning Contact: eric.mitchell@cs.stanford.edu Links: Paper | Website Keywords: model editing; meta-learning; language models; continual learning; temporal generalization Vision-Based Manipulators Need to Also See from Their Hands Authors: Kyle H