The International Conference on Machine Learning (ICML) 2020 is being hosted virtually from July 13th - July 18th. 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

Active World Model Learning in Agent-rich Environments with Progress Curiosity

Authors: Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins
Contact: khkim@cs.stanford.edu
Links: Video
Keywords: curiosity, active learning, world models, animacy, attention


Graph Structure of Neural Networks

Authors: Jiaxuan You, Jure Leskovec, Kaiming He, Saining Xie
Contact: jiaxuan@stanford.edu
Keywords: neural network design, network science, deep learning


A Distributional Framework For Data Valuation

Authors: Amirata Ghorbani, Michael P. Kim, James Zou
Contact: jamesz@stanford.edu
Links: Paper
Keywords: shapley value, data valuation, machine learning, data markets


A General Recurrent State Space Framework for Modeling Neural Dynamics During Decision-Making

Authors: David Zoltowski, Jonathan Pillow, Scott Linderman
Contact: scott.linderman@stanford.edu
Links: Paper
Keywords: computational neuroscience, dynamical systems, variational inference


An Imitation Learning Approach for Cache Replacement

Authors: Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn
Contact: evanliu@cs.stanford.edu
Links: Paper
Keywords: imitation learning, cache replacement, benchmark


An Investigation of Why Overparameterization Exacerbates Spurious Correlations

Authors: Shiori Sagawa*, Aditi Raghunathan*, Pang Wei Koh*, Percy Liang
Contact: ssagawa@cs.stanford.edu
Links: Paper
Keywords: robustness, spurious correlations, overparameterization


Better Depth-Width Trade-offs for Neural Networks through the Lens of Dynamical Systems.

Authors: Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas
Contact: vaggos@cs.stanford.edu
Links: Paper
Keywords: expressivity, depth, width, dynamical systems


Bridging the Gap Between f-GANs and Wasserstein GANs

Authors: Jiaming Song, Stefano Ermon
Contact: jiaming.tsong@gmail.com
Links: Paper
Keywords: gans, generative models, f-divergence, wasserstein distance


Concept Bottleneck Models

Authors: Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang
Contact: pangwei@cs.stanford.edu
Links: Paper
Keywords: concepts, intervention, interpretability


Domain Adaptive Imitation Learning

Authors: Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
Contact: khkim@cs.stanford.edu
Links: Paper
Keywords: imitation learning, domain adaptation, reinforcement learning, generative adversarial networks, cycle consistency


Encoding Musical Style with Transformer Autoencoders

Authors: Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse Engel
Contact: kechoi@cs.stanford.edu
Links: Paper | Blog Post | Video
Keywords: sequential, network, and time-series modeling; applications - music


Fair Generative Modeling via Weak Supervision

Authors: Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon
Contact: kechoi@cs.stanford.edu
Links: Paper | Video
Keywords: deep learning - generative models and autoencoders; fairness, equity, justice, and safety


Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods

Authors: Daniel Y. Fu, Mayee F. Chen, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, Christopher Ré
Contact: danfu@cs.stanford.edu
Links: Paper | Blog Post | Video
Keywords: weak supervision, latent variable models


Flexible and Efficient Long-Range Planning Through Curious Exploration

Authors: Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin Feigelis, Daniel Yamins
Contact: yamins@stanford.edu
Links: Paper | Blog Post | Video
Keywords: planning, deep learning, sparse reinforcement learning, curiosity


FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis

Authors: Aman Sinha, Matthew O’Kelly, Hongrui Zheng, Rahul Mangharam, John Duchi, Russ Tedrake
Contact: amans@stanford.edu, mokelly@seas.upenn.edu
Links: Paper | Video
Keywords: distributional robustness, online learning, autonomous driving, reinforcement learning, simulation, mcmc


Goal-Aware Prediction: Learning to Model what Matters

Authors: Suraj Nair, Silvio Savarese, Chelsea Finn
Contact: surajn@stanford.edu
Links: Paper
Keywords: reinforcement learning, visual planning, robotics


Graph-based, Self-Supervised Program Repair from Diagnostic Feedback

Authors: Michihiro Yasunaga, Percy Liang
Contact: myasu@cs.stanford.edu
Links: Paper | Blog Post | Video
Keywords: program repair, program synthesis, self-supervision, pre-training, graph


Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions

Authors: Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez
Contact: gottesman@fas.harvard.edu
Links: Paper
Keywords: reinforcement learning, off-policy evaluation, interpretability


Learning Near Optimal Policies with Low Inherent Bellman Error

Authors: Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer, Emma Brunskill
Contact: zanette@stanford.edu
Links: Paper
Keywords: reinforcement learning, exploration, function approximation


Maximum Likelihood With Bias-Corrected Calibration is Hard-To-Beat at Label Shift Domain Adaptation

Authors: Amr Alexandari*, Anshul Kundaje†, Avanti Shrikumar*† (*co-first †co-corresponding)
Contact: avanti@cs.stanford.edu, amr.alexandari@gmail.com, akundaje@stanford.edu
Links: Paper | Blog Post | Video
Keywords: domain adaptation, label shift, calibration, maximum likelihood


NGBoost: Natural Gradient Boosting for Probabilistic Prediction

Authors: Tony Duan*, Anand Avati*, Daisy Yi Ding, Sanjay Basu, Andrew Ng, Alejandro Schuler
Contact: avati@cs.stanford.edu
Links: Paper
Keywords: gradient boosting, uncertainty estimation, natural gradient


On the Expressivity of Neural Networks for Deep Reinforcement Learning

Authors: Kefan Dong, Yuping Luo, Tianhe Yu, Chelsea Finn, Tengyu Ma
Contact: kefandong@gmail.com
Links: Paper
Keywords: reinforcement learning


On the Generalization Effects of Linear Transformations in Data Augmentation

Authors: Sen Wu, Hongyang Zhang, Gregory Valiant, Christopher Ré
Contact: senwu@cs.stanford.edu
Links: Paper | Blog Post | Video
Keywords: data augmentation, generalization


Predictive Coding for Locally-Linear Control

Authors: Rui Shu*, Tung Nguyen*, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung Bui
Contact: ruishu@stanford.edu
Links: Paper | Video
Keywords: representation learning, information theory, generative models, planning, control


Robustness to Spurious Correlations via Human Annotations

Authors: Megha Srivastava, Tatsunori Hashimoto, Percy Liang
Contact: megha@cs.stanford.edu
Links: Paper
Keywords: robustness, distribution shift, crowdsourcing, human-in-the-loop


Sample Amplification: Increasing Dataset Size even when Learning is Impossible

Authors: Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant
Contact: shivamgarg@stanford.edu
Links: Paper | Video
Keywords: learning theory, sample amplification, generative models


Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM

Authors: Kunal Menda, Jean de Becdelièvre, Jayesh K. Gupta, Ilan Kroo, Mykel J. Kochenderfer, Zachary Manchester
Contact: kmenda@stanford.edu
Links: Paper | Video
Keywords: system identification; time series and sequence models


The Implicit and Explicit Regularization Effects of Dropout

Authors: Colin Wei, Sham Kakade, Tengyu Ma
Contact: colinwei@stanford.edu
Links: Paper
Keywords: dropout, deep learning theory, implicit regularization


Training Deep Energy-Based Models with f-Divergence Minimization

Authors: Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
Contact: lantaoyu@cs.stanford.edu
Links: Paper
Keywords: energy-based models; f-divergences; deep generative models


Two Routes to Scalable Credit Assignment without Weight Symmetry

Authors: Daniel Kunin*, Aran Nayebi*, Javier Sagastuy-Brena*, Surya Ganguli, Jonathan M. Bloom, Daniel L. K. Yamins
Contact: jvrsgsty@stanford.edu
Links: Paper | Video
Keywords: learning rules, computational neuroscience, machine learning


Understanding Self-Training for Gradual Domain Adaptation

Authors: Ananya Kumar, Tengyu Ma, Percy Liang
Contact: ananya@cs.stanford.edu
Links: Paper | Video
Keywords: domain adaptation, self-training, semi-supervised learning


Understanding and Mitigating the Tradeoff between Robustness and Accuracy

Authors: Aditi Raghunathan*, Sang Michael Xie*, Fanny Yang, John C. Duchi, Percy Liang
Contact: aditir@stanford.edu, xie@cs.stanford.edu
Links: Paper | Video
Keywords: adversarial examples, adversarial training, robustness, accuracy, tradeoff, robust self-training


Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling

Authors: Yao Liu, Pierre-Luc Bacon, Emma Brunskill
Contact: yaoliu@stanford.edu
Links: Paper
Keywords: reinforcement learning, off-policy evaluation, importance sampling


Visual Grounding of Learned Physical Models

Authors: Yunzhu Li, Toru Lin*, Kexin Yi*, Daniel M. Bear, Daniel L. K. Yamins, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba
Contact: liyunzhu@mit.edu
Links: Paper | Video
Keywords: intuitive physics, visual grounding, physical reasoning


Learning to Simulate Complex Physics with Graph Networks

Authors: Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia
Contact: rexying@stanford.edu
Links: Paper
Keywords: simulation, graph neural networks


Coresets for Data-Efficient Training of Machine Learning Models

Authors: Baharan Mirzasoleiman, Jeff Bilmes, Jure Leskovec
Contact: baharanm@cs.stanford.edu
Links: Paper | Video
Keywords: Coresets, Data-efficient training, Submodular optimization, Incremental gradient methods


Which Tasks Should be Learned Together in Multi-Task Learning

Authors: Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, Silvio Savarese
Contact: tstand@cs.stanford.edu
Links: Paper | Video
Keywords: machine learning, multi-task learning, computer vision


Accelerated Message Passing for Entropy-Regularized MAP Inference

Authors: Jonathan N. Lee, Aldo Pacchiano, Peter Bartlett, Michael I. Jordan
Contact: jnl@stanford.edu
Links: Paper
Keywords: graphical models, map inference, message passing, optimization


On Learning Sets of Symmetric Elements

Authors: Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya
Contact: or.litany@gmail.com
Links: Paper
Keywords: equivariance, sets, pointclouds, graphs
Outstanding Paper Award


Individual Calibration with Randomized Forecasting

Authors: Shengjia Zhao, Tengyu Ma, Stefano Ermon
Contact: sjzhao@stanford.edu
Links: Paper
Keywords: calibration, uncertainty estimation


We look forward to seeing you at ICML 2020!