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
paper
Suraj Nair, Chelsea Finn | contact: surajn@stanford.edu
keywords: visual planning; reinforcement learning; robotics
Active World Model Learning with Progress Curiosity
paper
Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Dan Yamins | contact: khkim@cs.stanford.edu
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: trid@stanford.edu
keywords: structured matrices, efficient ml, algorithms, butterfly matrices, arithmetic circuits
Weakly Supervised Disentanglement with Guarantees
paper
Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole | contact: ruishu@stanford.edu
keywords: disentanglement, generative models, weak supervision, representation learning, theory
Depth width tradeoffs for Relu networks via Sharkovsky’s theorem
paper
Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas, Xiao Wang | contact: vaggos@cs.stanford.edu
keywords: dynamical systems, benefits of depth, expressivity
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
paper
Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn | contact: ayz@stanford.edu
keywords: imitation learning, meta-learning, reinforcement learning
Assessing robustness to noise: low-cost head CT triage
paper
Sarah Hooper, Jared Dunnmon, Matthew Lungren, Sanjiv Sam Gambhir, Christopher Ré, Adam Wang, Bhavik Patel | contact: smhooper@stanford.edu
keywords: ai for affordable healthcare workshop, medical imaging, sinogram, ct, image noise
Learning transport cost from subset correspondence
paper
Ruishan Liu, Akshay Balsubramani, James Zou | contact: ruishan@stanford.edu
keywords: optimal transport, data alignment, metric learning
Generalization through Memorization: Nearest Neighbor Language Models
paper
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis | contact: urvashik@stanford.edu
keywords: language models, k-nearest neighbors
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
paper
Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, Percy Liang | contact: ssagawa@cs.stanford.edu
keywords: distributionally robust optimization, deep learning, robustness, generalization, regularization
Phase Transitions for the Information Bottleneck in Representation Learning
paper
Tailin Wu, Ian Fischer | contact: tailin@cs.stanford.edu
keywords: information theory, representation learning, phase transition
Improving Neural Language Generation with Spectrum Control
paper
Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu | contact: jhuang18@stanford.edu
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: senwu@cs.stanford.edu
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: weihuahu@cs.stanford.edu
keywords: pre-training, transfer learning, graph neural networks
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
paper
Hongyu Ren, Weihua Hu, Jure Leskovec | contact: hyren@cs.stanford.edu
keywords: knowledge graph embeddings
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling
paper
Yuping Luo, Huazhe Xu, Tengyu Ma | contact: roosephu@gmail.com
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
paper
Colin Wei, Tengyu Ma | contact: colinwei@stanford.edu
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: cody@cs.stanford.edu
keywords: active learning, data selection, deep learning
We look forward to seeing you at ICLR!