The Neural Information Processing Systems (NeurIPS) 2020 conference is being hosted virtually from Dec 6th - Dec 12th. 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

Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration

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

Acceleration with a Ball Optimization Oracle

Authors: Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian
Contact: kjtian@stanford.edu
Award nominations: Oral presentation
Links: Paper
Keywords: convex optimization, local search, trust region methods

BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits

Authors: Mo Tiwari, Martin Jinye Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony
Contact: Motiwari@stanford.edu
Links: Paper | Video
Keywords: clustering, k-means, k-medoids, multi-armed bandits

CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations

Authors: Davis Rempe, Tolga Birdal, Yongheng Zhao, Zan Gojcic, Srinath Sridhar, Leonidas J. Guibas
Contact: drempe@stanford.edu
Links: Paper | Video | Website
Keywords: 3d vision, dynamic point clouds, representation learning

Compositional Explanations of Neurons

Authors: Jesse Mu, Jacob Andreas
Contact: muj@stanford.edu
Award nominations: oral
Links: Paper
Keywords: interpretability, explanation, deep learning, computer vision, natural language processing, adversarial examples

Continuous Meta-Learning without Tasks

Authors: James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone
Contact: jharrison@stanford.edu
Links: Paper
Keywords: meta-learning, continuous learning, changepoint detection

Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel

Authors: Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli
Contact: sfort1@stanford.edu
Links: Paper
Keywords: loss landscape, neural tangent kernel, linearization, taylorization, basin, nonlinear advantage

Diversity can be Transferred: Output Diversification for White- and Black-box Attacks

Authors: Yusuke Tashiro, Yang Song, Stefano Ermon
Contact: ytashiro@stanford.edu
Links: Paper | Website
Keywords: adversarial examples, deep learning, robustness

Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders

Authors: Masha Itkina, Boris Ivanovic, Ransalu Senanayake, Mykel J. Kochenderfer, and Marco Pavone
Contact: mitkina@stanford.edu
Links: Paper | Website
Keywords: sparse distributions, generative models, discrete latent spaces, behavior prediction, image generation

Federated Accelerated Stochastic Gradient Descent

Authors: Honglin Yuan, Tengyu Ma
Contact: yuanhl@stanford.edu
Award nominations: Best Paper Award of Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020 (FL-ICML’20)
Links: Paper | Website
Keywords: federated learning, local sgd, acceleration, fedac

Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics

Authors: Alex Michael Tseng, Avanti Shrikumar, Anshul Kundaje
Contact: amtseng@stanford.edu
Links: Paper | Website
Keywords: deep learning, interpretability, attribution prior, computational biology, genomics

From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering

Authors: Ines Chami, Albert Gu, Vaggos Chatziafratis, Christopher Ré
Contact: chami@stanford.edu
Links: Paper | Video | Website
Keywords: hierarchical clustering, hyperbolic embeddings

FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply

Authors: Lingjiao Chen; Matei Zaharia; James Zou
Contact: lingjiao@stanford.edu
Award nominations: Oral Presentation
Links: Paper | Blog Post | Website
Keywords: machine learning as a service, ensemble learning, meta learning, systems for machine learning

Generative 3D Part Assembly via Dynamic Graph Learning

Authors: Jialei Huang, Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas Guibas, Hao Dong
Contact: fqnchina@gmail.com
Links: Paper
Keywords: 3d part assembly, dynamic graph learning

Generative 3D Part Assembly via Dynamic Graph Learning

Authors: Jialei Huang*, Guanqi Zhan*, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas J. Guibas, Hao Dong
Contact: kaichun@cs.stanford.edu
Links: Paper | Website
Keywords: 3d part assembly, graph neural network

Gradient Surgery for Multi-Task Learning

Authors: Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn
Contact: tianheyu@cs.stanford.edu
Links: Paper | Website
Keywords: multi-task learning, deep reinforcement learning

HiPPO: Recurrent Memory with Optimal Polynomial Projections

Authors: Albert Gu*, Tri Dao*, Stefano Ermon, Atri Rudra, Chris Ré
Contact: albertgu@stanford.edu, trid@stanford.edu
Links: Paper | Blog Post
Keywords: representation learning, time series, recurrent neural networks, lstm, orthogonal polynomials

Identifying Learning Rules From Neural Network Observables

Authors: Aran Nayebi, Sanjana Srivastava, Surya Ganguli, Daniel L.K. Yamins
Contact: anayebi@stanford.edu
Award nominations: Spotlight Presentation
Links: Paper | Website
Keywords: computational neuroscience, learning rule, deep networks

Improved Techniques for Training Score-Based Generative Models

Authors: Yang Song, Stefano Ermon
Contact: songyang@stanford.edu
Links: Paper
Keywords: score-based generative modeling, score matching, deep generative models

Language Through a Prism: A Spectral Approach for Multiscale Language Representations

Authors: Alex Tamkin, Dan Jurafsky, Noah Goodman
Contact: atamkin@stanford.edu
Links: Paper
Keywords: bert, signal processing, self-supervised learning, interpretability, multiscale

Large-Scale Methods for Distributionally Robust Optimization

Authors: Daniel Levy, Yair Carmon, John Duchi, Aaron Sidford
Contact: danilevy@stanford.edu
Links: Paper
Keywords: robustness dro optimization large-scale optimal

Learning Physical Graph Representations from Visual Scenes

Authors: Daniel Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li F. Fei-Fei, Jiajun Wu, Josh Tenenbaum, Daniel L. Yamins
Contact: dbear@stanford.edu
Links: Paper | Blog Post | Website
Keywords: structure learning, graph learning, visual scene representations, unsupervised learning, unsupervised segmentation, object-centric representation, intuitive physics

MOPO: Model-based Offline Policy Optimization

Authors: Tianhe Yu*, Garrett Thomas*, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma
Contact: tianheyu@cs.stanford.edu
Links: Paper | Website
Keywords: offline reinforcement learning, model-based reinforcement learning

Measuring Robustness to Natural Distribution Shifts in Image Classification

Authors: Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt
Contact: rtaori@stanford.edu
Award nominations: Spotlight
Links: Paper | Website
Keywords: machine learning, robustness, image classification

Minibatch Stochastic Approximate Proximal Point Methods

Authors: Hilal Asi, Karan Chadha, Gary Cheng, John Duchi
Contact: chenggar@stanford.edu
Award nominations: Spotlight talk
Links: Paper
Keywords: stochastic optimization, sgd, aprox

Model-based Adversarial Meta-Reinforcement Learning

Authors: Zichuan Lin, Garrett Thomas, Guangwen Yang, Tengyu Ma
Contact: lzcthu12@gmail.com,gwthomas@stanford.edu
Links: Paper
Keywords: model-based rl, meta-rl, minimax

Multi-Plane Program Induction with 3D Box Priors

Authors: Yikai Li, Jiayuan Mao, Xiuming Zhang, William T. Freeman, Joshua B. Tenenbaum, Noah Snavely, Jiajun Wu
Contact: jiajunwu@cs.stanford.edu
Links: Paper | Video | Website
Keywords: visual program induction, 3d vision, image editing

Multi-label Contrastive Predictive Coding

Authors: Jiaming Song, Stefano Ermon
Contact: jiaming.tsong@gmail.com
Links: Paper
Keywords: representation learning, mutual information

Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems

Authors: Aman Sinha, Matthew O’Kelly, Russ Tedrake, John Duchi
Contact: amans@stanford.edu
Links: Paper
Keywords: safety, probabilistic methods, autonomous systems

Neuron Shapley: Discovering the Responsible Neurons

Authors: Amirata Ghorbani, James Zou
Contact: amiratag@stanford.edu
Links: Paper
Keywords: interpretability, deep learning, shapley value

No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems

Authors: Nimit Sharad Sohoni, Jared Alexander Dunnmon, Geoffrey Angus, Albert Gu, Christopher Ré
Contact: nims@stanford.edu
Links: Paper | Blog Post | Video
Keywords: classification, robustness, clustering, neural feature representations

Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding

Authors: Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill
Contact: keramati@stanford.edu
Links: Paper
Keywords: off-policy policy evaluation, unobserved confounding, reinforcement learning

One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL

Authors: Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn
Contact: szk@stanford.edu
Links: Paper
Keywords: robustness, diversity, reinforcement learning

Point process models for sequence detection in high-dimensional neural spike trains

Authors: Alex H. Williams, Anthony Degleris, Yixin Wang, Scott W. Linderman
Contact: ahwillia@stanford.edu
Award nominations: Selected for Oral Presentation
Links: Paper | Website
Keywords: bayesian nonparametrics, unsupervised learning

Predictive coding in balanced neural networks with noise, chaos and delays

Authors: Jonathan Kadmon, Jonathan Timcheck, Surya Ganguli
Contact: kadmonj@stanford.edu
Links: Paper
Keywords: neuroscience, predictive coding, chaos

Probabilistic Circuits for Variational Inference in Discrete Graphical Models

Authors: Andy Shih, Stefano Ermon
Contact: andyshih@stanford.edu
Links: Paper
Keywords: variational inference, discrete, high-dimensions, sum product networks, probabilistic circuits, graphical models

Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration

Authors: Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill.
Contact: yaoliu@stanford.edu
Links: Paper
Keywords: reinforcement leanring, off-policy, batch reinforcement learning

Pruning neural networks without any data by iteratively conserving synaptic flow

Authors: Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, Surya Ganguli
Contact: kunin@stanford.edu
Links: Paper | Video | Website
Keywords: network pruning, sparse initialization, lottery ticket

Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing

Authors: Arun Jambulapati, Jerry Li, Kevin Tian
Contact: kjtian@stanford.edu
Award nominations: Spotlight presentation
Links: Paper
Keywords: robust statistics, principal component analysis, positive semidefinite programming

Self-training Avoids Using Spurious Features Under Domain Shift

Authors: Yining Chen*, Colin Wei*, Ananya Kumar, Tengyu Ma (*equal contribution)
Contact: cynnjjs@stanford.edu
Links: Paper
Keywords: self-training, pseudo-labeling, domain shift, robustness

Wasserstein Distances for Stereo Disparity Estimation

Authors: Divyansh Garg, Yan Wang, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
Contact: divgarg@stanford.edu
Award nominations: Spotlight
Links: Paper | Video | Website
Keywords: depth estimation, disparity estimation, autonomous driving, 3d object detection, statistical learning

We look forward to seeing you at NeurIPS2020!