Stanford AI Lab Papers and Talks at ICML 2020

July 11, 2020

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
Links: Paper
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

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