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!