SAIL at ICLR 2020: Accepted Papers and Videos

The International Conference on Learning Representations (ICLR) 2020 is being hosted virtually from April 26th – May 1st. 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

Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation


Suraj Nair, Chelsea Finn | contact:
keywords: visual planning; reinforcement learning; robotics

Active World Model Learning with Progress Curiosity


Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Dan Yamins | contact:
keywords: curiosity, reinforcement learning, cognitive science

Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps

paper | blog post

Tri Dao, Nimit Sohoni, Albert Gu, Matthew Eichhorn, Amit Blonder, Megan Leszczynski, Atri Rudra, Christopher Ré | contact:
keywords: structured matrices, efficient ml, algorithms, butterfly matrices, arithmetic circuits

Weakly Supervised Disentanglement with Guarantees


Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole | contact:
keywords: disentanglement, generative models, weak supervision, representation learning, theory

Depth width tradeoffs for Relu networks via Sharkovsky’s theorem


Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas, Xiao Wang | contact:
keywords: dynamical systems, benefits of depth, expressivity

Watch, Try, Learn: Meta-Learning from Demonstrations and Reward


Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn | contact:
keywords: imitation learning, meta-learning, reinforcement learning

Assessing robustness to noise: low-cost head CT triage


Sarah Hooper, Jared Dunnmon, Matthew Lungren, Sanjiv Sam Gambhir, Christopher Ré, Adam Wang, Bhavik Patel | contact:
keywords: ai for affordable healthcare workshop, medical imaging, sinogram, ct, image noise

Learning transport cost from subset correspondence


Ruishan Liu, Akshay Balsubramani, James Zou | contact:
keywords: optimal transport, data alignment, metric learning

Generalization through Memorization: Nearest Neighbor Language Models


Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis | contact:
keywords: language models, k-nearest neighbors

Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization


Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, Percy Liang | contact:
keywords: distributionally robust optimization, deep learning, robustness, generalization, regularization

Phase Transitions for the Information Bottleneck in Representation Learning


Tailin Wu, Ian Fischer | contact:
keywords: information theory, representation learning, phase transition

Improving Neural Language Generation with Spectrum Control


Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu | contact:
keywords: neural language generation, pre-trained language model, spectrum control

Understanding and Improving Information Transfer in Multi-Task Learning

paper | blog post

Sen Wu, Hongyang Zhang, Christopher Ré | contact:
keywords: multi-task learning

Strategies for Pre-training Graph Neural Networks

paper | blog post

Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec | contact:
keywords: pre-training, transfer learning, graph neural networks

Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings


Hongyu Ren, Weihua Hu, Jure Leskovec | contact:
keywords: knowledge graph embeddings

Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling


Yuping Luo, Huazhe Xu, Tengyu Ma | contact:
keywords: imitation learning, model-based imitation learning, model-based rl, behavior cloning, covariate shift

Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin


Colin Wei, Tengyu Ma | contact:
keywords: deep learning theory, generalization bounds, adversarially robust generalization, data-dependent generalization bounds

Selection via Proxy: Efficient Data Selection for Deep Learning

paper | blog post | code

Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia | contact:
keywords: active learning, data selection, deep learning

We look forward to seeing you at ICLR!

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