Skip to content
Home » Stanford AI Lab Papers and Talks at ICLR 2022

Stanford AI Lab Papers and Talks at ICLR 2022

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 Hsu, Moo Jin Kim, Rafael Rafailov, Jiajun Wu, Chelsea Finn

Contact: kylehsu@cs.stanford.edu

Award nominations: Oral Presentation

Links: Paper | Website

Keywords: reinforcement learning, observation space, out-of-distribution generalization, visuomotor control, robotics, manipulation

IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes

Authors: Qi Li*, Kaichun Mo*, Yanchao Yang, Hang Zhao, Leonidas J. Guibas

Contact: kaichun@cs.stanford.edu

Links: Paper

Keywords: embodied ai, 3d scene graph, interactive perception

VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects

Authors: Ruihai Wu*, Yan Zhao*, Kaichun Mo*, Zizheng Guo, Yian Wang, Tianhao Wu, Qingnan Fan, Xuelin Chen, Leonidas J. Guibas, Hao Dong

Contact: kaichun@cs.stanford.edu

Links: Paper | Video | Website

Keywords: visual affordance learning, robotic manipulation, 3d perception, interactive perception

Language modeling via stochastic processes

Authors: Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori Hashimoto

Contact: rewang@stanford.edu

Award nominations: Oral Presentation

Links: Paper | Video | Website

Keywords: contrastive learning, language modeling, stochastic processes

MetaMorph: Learning Universal Controllers with Transformers

Authors: Agrim Gupta, Linxi Fan, Surya Ganguli, Li Fei-Fei

Contact: agrim@stanford.edu

Links: Paper | Video | Website

Keywords: rl, modular robots, transformers

Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Authors: Ananya Kumar

Contact: ananya@cs.stanford.edu

Award nominations: Oral Presentation

Links: Paper

Keywords: fine-tuning theory, transfer learning theory, fine-tuning, distribution shift, implicit regularization

An Experimental Design Perspective on Model-Based Reinforcement Learning

Authors: Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger

Contact: virajm@cs.cmu.edu, neiswanger@cs.stanford.edu

Links: Paper

Keywords: reinforcement learning, model-based reinforcement learning, mbrl, bayesian optimal experimental design, boed, bax

Domino: Discovering Systematic Errors with Cross-Modal Embeddings

Authors: Sabri Eyuboglu*, Maya Varma*, Khaled Saab*, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, Christopher Ré

Contact: {eyuboglu,mvarma2,ksaab}@stanford.edu

Award nominations: Oral Presentation

Links: Paper | Blog Post | Website

Keywords: robustness, subgroup analysis, error analysis, multimodal, slice discovery

Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models

Authors: Tri Dao, Beidi Chen, Kaizhao Liang, Jiaming Yang, Zhao Song, Atri Rudra, Christopher Ré

Contact: trid@stanford.edu

Award nominations: Spotlight

Links: Paper | Blog Post

Keywords: sparse training, butterfly matrices

Hindsight: Posterior-guided training of retrievers for improved open-ended generation

Authors: Ashwin Paranjape, Omar Khattab, Christopher Potts, Matei Zaharia, Christopher D Manning

Contact: ashwinp@cs.stanford.edu

Links: Paper

Keywords: retrieval, generation, retrieval-augmented generation, open-ended generation, informative conversations, free-form qa, posterior distribution, elbo

Unsupervised Discovery of Object Radiance Fields

Authors: Hong-Xing Yu, Leonidas J. Guibas, Jiajun Wu

Contact: koven@cs.stanford.edu

Links: Paper | Video | Website

Keywords: object-centric representation, unsupervised, 3d object discovery

Efficiently Modeling Long Sequences with Structured State Spaces

Authors: Albert Gu, Karan Goel, Christopher Ré

Contact: albertgu@stanford.edu

Award nominations: Outstanding Paper Honorable Mention

Links: Paper | Blog Post | Video

Keywords: hippo

How many degrees of freedom do we need to train deep networks: a loss landscape perspective

Authors: Brett W. Larsen, Stanislav Fort, Nic Becker, Surya Ganguli

Contact: bwlarsen@stanford.edu

Links: Paper

Keywords: loss landscape, high-dimensional geometry, random hyperplanes, optimization

How did the Model Change? Efficiently Assessing Machine Learning API Shifts

Authors: Lingjiao Chen, Matei Zaharia, James Zou

Contact: lingjiao@stanford.edu

Links: Paper | Website

Keywords: mlaas, performance shifts, ml systems

We look forward to seeing you at ICLR 2022!

Generated by Feedzy