The thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) 2021 is being hosted virtually from Dec 6th - 14th. We’re excited to share all the work from SAIL that’s being presented at the main conference, at the Datasets and Benchmarks track and the various workshops, and you’ll find links to papers, videos and blogs below.

Some of the members in our SAIL community also serve as co-organizers of several exciting workshops that will take place on Dec 13-14, so we hope you will check them out!

Feel free to reach out to the contact authors and the workshop organizers directly to learn more about the work that’s happening at Stanford!

Main Conference

Improving Compositionality of Neural Networks by Decoding Representations to Inputs

Authors: Mike Wu, Noah Goodman, Stefano Ermon
Contact: wumike@stanford.edu
Links: Paper
Keywords: generative models, compositionality, decoder

Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems

Authors: Jimmy T.H. Smith, Scott W. Linderman, David Sussillo
Contact: jsmith14@stanford.edu
Links: Paper | Website
Keywords: recurrent neural networks, switching linear dynamical systems, interpretability, fixed points

Compositional Transformers for Scene Generation

Authors: Drew A. Hudson, C. Lawrence Zitnick
Contact: dorarad@cs.stanford.edu
Links: Paper | Github
Keywords: GANs, transformers, compositionality, scene synthesis

Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers

Authors: Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, Chris Ré
Contact: albertgu@stanford.edu
Links: Paper
Keywords: recurrent neural networks, rnn, continuous models, state space, long range dependencies, sequence modeling

Emergent Communication of Generalizations

Authors: Jesse Mu, Noah Goodman
Contact: muj@stanford.edu
Links: Paper | Video
Keywords: emergent communication, multi-agent communication, language grounding, compositionality

Deep Learning on a Data Diet: Finding Important Examples Early in Training

Authors: Mansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite
Contact: mansheej@stanford.edu
Links: Paper
Keywords: data pruning

ELLA: Exploration through Learned Language Abstraction

Authors: Suvir Mirchandani, Siddharth Karamcheti, Dorsa Sadigh
Contact: suvir@cs.stanford.edu
Links: Paper | Video
Keywords: instruction following, reward shaping, reinforcement learning

CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

Authors: Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon
Contact: ytashiro@stanford.edu
Links: Paper | Website
Keywords: score-based generative modeling, time series imputation

Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality

Authors: Songyuan Zhang, Zhangjie Cao, Dorsa Sadigh, Yanan Sui
Contact: szhang21@mit.edu
Links: Paper | Video | Website
Keywords: imitation learning, learning from demonstration, learning from suboptimal demonstrations

Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks

Authors: Aran Nayebi, Alexander Attinger, Malcolm G. Campbell, Kiah Hardcastle, Isabel I.C. Low, Caitlin S. Mallory, Gabriel C. Mel, Ben Sorscher, Alex H. Williams, Surya Ganguli, Lisa M. Giocomo, Daniel L.K. Yamins
Contact: anayebi@stanford.edu
Award nominations: Spotlight Presentation
Links: Paper | Website
Keywords: neural coding, medial entorhinal cortex, grid cells, biologically-inspired navigation, path integration, recurrent neural networks

On the theory of reinforcement learning with once-per-episode feedback

Authors: Niladri Chatterji, Aldo Pacchiano, Peter Bartlett, Michael Jordan
Contact: niladri@cs.stanford.edu
Keywords: theoretical reinforcement learning, binary rewards, non-markovian rewards

HyperSPNs: Compact and Expressive Probabilistic Circuits

Authors: Andy Shih, Dorsa Sadigh, Stefano Ermon
Contact: andyshih@stanford.edu
Links: Paper | Video | Website
Keywords: generative models, tractable probabilistic models, sum product networks, probabilistic circuits

COMBO: Conservative Offline Model-Based Policy Optimization

Authors: Tianhe Yu*, Aviral Kumar*, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn
Contact: tianheyu@cs.stanford.edu
Links: Paper
Keywords: offline reinforcement learning, model-based reinforcement learning, deep reinforcement learning

Conservative Data Sharing for Multi-Task Offline Reinforcement Learning

Authors: Tianhe Yu*, Aviral Kumar*, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn
Contact: tianheyu@cs.stanford.edu
Links: Paper
Keywords: offline reinforcement learning, multi-task reinforcement learning, deep reinforcement learning

Autonomous Reinforcement Learning via Subgoal Curricula

Authors: Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn
Contact: architsh@stanford.edu
Links: Paper | Website
Keywords: reinforcement learning, curriculum, autonomous learning, reset-free reinforcement learning

Lossy Compression for Lossless Prediction

Authors: Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich Chris J. Maddison
Contact: yanndubs@stanford.edu
Award nominations: Spotlight Presentation
Links: Paper | Video | Website
Keywords: compression, invariances, information theory, machine learning, self-supervised learning

Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations

Authors: Joy Hsu, Jeffrey Gu, Gong-Her Wu, Wah Chiu, Serena Yeung
Contact: joycj@stanford.edu
Links: Paper
Keywords: hyperbolic representations, hierarchical structure, biomedical

Estimating High Order Gradients of the Data Distribution by Denoising

Authors: Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon
Contact: chenlin@stanford.edu
Keywords: score matching, langevin dynamics, denoising, generative modeling

Universal Off-Policy Evaluation

Authors: Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik Learned-Miller, Emma Brunskill, Philip Thomas
Contact: ychandak@cs.umass.edu
Links: Paper | Website
Keywords: metrics, risk, distribution, cdf, off-policy evaluation, ope, reinforcement learning, counterfactuals, high-confidence bounds, confidence intervals

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models

Authors: Phil Chen, Masha Itkina, Ransalu Senanayake, Mykel J. Kochenderfer
Contact: philhc@stanford.edu
Links: Paper
Keywords: deep learning or neural networks, sparsity and feature selection, variational inference, (application) natural language and text processing

Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss

Authors: Jeff Z. HaoChen, Colin Wei, Adrien Gaidon, Tengyu Ma
Contact: jhaochen@stanford.edu
Links: Paper
Keywords: deep learning theory, unsupervised learning theory, representation learning theory

Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature

Authors: Kefan Dong, Jiaqi Yang, Tengyu Ma
Contact: kefandong@stanford.edu
Links: Paper | Video
Keywords: nonlinear bandits, online learning, deep reinforcement learning theory, sequential rademacher complexity

Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP

Authors: Joshua Rozner, Christopher Potts, Kyle Mahowald
Contact: rozner@stanford.edu
Links: Paper | Website
Keywords: compositionality in language, curriculum learning, meta-linguistics, systematicity, generalization

Design of Experiments for Stochastic Contextual Linear Bandits

Authors: Andrea Zanette*, Kefan Dong*, Jonathan Lee*, Emma Brunskill
Contact: zanette@berkeley.edu
Links: Paper
Keywords: linear bandits, design of experiments

Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning

Authors: Andrea Zanette, Martin J. Wainwright, Emma Brunskill
Contact: zanette@berkeley.edu
Links: Paper
Keywords: offline rl, mirror descent, bellman closure

A Topological Perspective on Causal Inference

Authors: Duligur Ibeling, Thomas Icard
Contact: icard@stanford.edu
Links: Paper
Keywords: causal inference, topological learning theory

Adversarial Training Helps Transfer Learning via Better Representations

Authors: Zhun Deng, Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, James Zou
Contact: jamesyzou@gmail.com
Links: Paper
Keywords: transfer learning, adversarial training

Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation

Authors: Lin Guan,Mudit Verma,Sihang Guo,Ruohan Zhang,Subbarao Kambhampati
Contact: zharu@stanford.edu
Award nominations: Spotlight
Links: Paper | Website
Keywords: human-in-the-loop reinforcement learning, evaluative feedback, saliency map, visual explanation

Machine versus Human Attention in Deep Reinforcement Learning Tasks

Authors: Sihang Guo, Ruohan Zhang, Bo Liu, Yifeng Zhu, Dana Ballard, Mary Hayhoe, Peter Stone
Contact: zharu@stanford.edu
Links: Paper
Keywords: deep reinforcement learning, interpretability, attention, eye tracking

Play to Grade: Testing Coding Games as Classifying Markov Decision Process

Authors: Allen Nie, Emma Brunskill, Chris Piech
Contact: anie@stanford.edu
Links: Paper | Website
Keywords: reinforcement learning, computational education, collaborative training, markov decision process

The Value of Information When Deciding What to Learn

Authors: Dilip Arumugam, Benjamin Van Roy
Contact: dilip@cs.stanford.edu
Links: Paper
Keywords: exploration, information theory, multi-armed bandits, reinforcement learning

Diversity Matters When Learning From Ensembles

Authors: Giung Nam*, Jongmin Yoon*, Yoonho Lee, Juho Lee
Contact: yoonho@cs.stanford.edu
Links: Paper | Website
Keywords: deep ensembles, knowledge distillation, calibration, output diversified sampling, batchensemble

Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes

Authors: HyunJi Nam, Scott Fleming, Emma Brunskill
Contact: scottyf@stanford.edu
Links: Paper | Website
Keywords: reinforcement learning, observation cost, markov decision process, mdp, partially observable markov decision process, pomdp, probably approximately correct, pac, healthcare, health care

Meta-learning with an Adaptive Task Scheduler

Authors: Huaxiu Yao, Yu Wang, Ying Wei, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn
Contact: huaxiu@cs.stanford.edu
Links: Paper
Keywords: adaptive task scheduler, meta-learning, sampling

Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis

Authors: Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon
Contact: kellyyhe@stanford.edu
Links: Paper | Video | Website
Keywords: remote sensing, super-resolution, generative models

Scatterbrain: Unifying Sparse and Low-rank Attention

Authors: Beidi Chen*, Tri Dao*, Eric Winsor, Zhao Song, Atri Rudra, Christopher Ré.
Contact: trid@stanford.edu
Links: Paper
Keywords: efficient attention, sparse, low-rank

BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery

Authors: Chris Cundy, Aditya Grover, Stefano Ermon
Contact: cundy@stanford.edu
Keywords: causal inference, variational inference

Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration

Authors: Shengjia Zhao, Michael P Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon
Contact: sjzhao@stanford.edu
Links: Paper
Keywords: calibration, decision making under uncertainty

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification

Authors: Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider
Contact: youngsec@andrew.cmu.edu, neiswanger@cs.stanford.edu
Links: Paper | Website
Keywords: uncertainty quantification, uq, quantile regression, pinball loss

Causal Abstractions of Neural Networks

Authors: Atticus Geiger*, Hanson Lu*, Thomas Icard, Christopher Potts
Contact: atticusg@stanford.edu
Links: Paper
Keywords: interpretability, analysis, nlp, causality

Generalized Shape Metrics on Neural Representations

Authors: Alex H Williams, Erin Kunz, Simon Kornblith, Scott Linderman
Contact: alex.h.willia@gmail.com
Keywords: representational similarity analysis, neural representations, shape analysis, metric space

D2C: Diffusion-Denoising Models for Few-shot Conditional Generation

Authors: Abhishek Sinha*, Jiaming Song*, Chenlin Meng, Stefano Ermon
Contact: tsong@cs.stanford.edu
Links: Paper | Website
Keywords: generative modeling, contrastive learning, conditional generation

Combiner: Full Attention Transformer with Sparse COmputation Cost

Authors: Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai
Contact: hyren@cs.stanford.edu
Links: Paper
Keywords: efficient transformer

Maximum Likelihood Training of Score-Based Diffusion Models

Authors: Yang Song, Conor Durkan, Iain Murray, Stefano Ermon
Contact: yangsong@cs.stanford.edu
Award nominations: Spotlight presentation
Links: Paper
Keywords: score-based generative models, denoising score matching, diffusion models, maximum likelihood training

Contrastive Reinforcement Learning of Symbolic Reasoning Domains

Authors: Gabriel Poesia, WenXin Dong, Noah Goodman
Contact: poesia@stanford.edu
Keywords: reinforcement learning, education, contrastive learning, symbolic reasoning

Equivariant Manifold Flows

Authors: Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser Nam Lim, Christopher M. De Sa
Contact: aaronlou@stanford.edu
Links: Paper | Website
Keywords: manifold, normalizing flow, equivariant, invariant

Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions

Authors: Yin Tat Lee, Ruoqi Shen, Kevin Tian
Contact: kjtian@stanford.edu
Award nominations: Oral presentation
Links: Paper | Video
Keywords: sampling, lower bounds, langevin dynamics, hamiltonian monte carlo

List-Decodable Mean Estimation in Nearly-PCA Time

Authors: Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian
Contact: kjtian@stanford.edu
Award nominations: Spotlight presentation
Links: Paper
Keywords: robust statistics, semidefinite programming, mixture models

Robust Regression Revisited: Acceleration and Improved Estimation Rates

Authors: Arun Jambulapati, Jerry Li, Tselil Schramm, Kevin Tian
Contact: kjtian@stanford.edu
Links: Paper
Keywords: robust statistics, regression, generalized linear models, acceleration, sum of squares methods

Learning with User-Level Privacy

Authors: Daniel Levy*, Ziteng Sun*, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
Contact: danilevy@stanford.edu
Links: Paper
Keywords: differential privacy user-level

Adapting to Function Difficulty and Growth Conditions in Private Optimization

Authors: Hilal Asi*, Daniel Levy*, John C. Duchi
Contact: asi@stanford.edu
Links: Paper
Keywords: differential privacy adaptivity optimization

Imitation with Neural Density Models

Authors: Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon
Contact: khkim@cs.stanford.edu
Links: Paper
Keywords: rl; imitation learning; density estimation

Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning

Authors: Colin Wei, Sang Michael Xie, Tengyu Ma
Contact: colinwei@stanford.edu
Links: Paper
Keywords: nlp pretraining, theoretical analysis

Safe Reinforcement Learning by Imagining the Near Future

Authors: Garrett Thomas, Yuping Luo, Tengyu Ma
Contact: gwthomas@stanford.edu
Links: Paper
Keywords: safe exploration, model-based rl

Pseudo-Spherical Contrastive Divergence

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

IQ-Learn: Inverse soft-Q Learning for Imitation

Authors: Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Stefano Ermon
Contact: divgarg@stanford.edu
Award nominations: Spotlight
Links: Paper | Website
Keywords: reinforcement learning, imitation learning, inverse reinforcement learning, statistical learning, energy-based models

Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks

Authors: Tolga Birdal ~Tolga_Birdal3 , Aaron Lou, Leonidas Guibas, Umut Simsekli
Contact: aaronlou@stanford.edu
Links: Paper | Website
Keywords: generalization, persistent homology, intrinsic dimension, deep networks

Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval

Authors: Omar Khattab, Christopher Potts, Matei Zaharia
Contact: okhattab@stanford.edu
Award nominations: Spotlight paper
Links: Paper | Blog Post
Keywords: neural retrieval, multi-hop question answering, claim verification, reasoning, colbert

Datasets and Benchmarks Track

Workshops

This year, multiple members of the SAIL community are also involved in great workshops that will take place on Dec 13-14. We hope you’ll check them out!

Machine Learning for Structural Biology Workshop (Dec 13)


Organizers: Namrata Anand, Bonnie Berger, Wouter Boomsma, Erika DeBenedictis, Stephan Eismann, John Ingraham, Sergey Ovchinnikov, Roshan Rao, Raphael Townshend and Ellen Zhong

Controllable Generative Modeling in Language and Vision (CtrlGen Workshop) (Dec 13)


Organizers: Steven Y. Feng, Drew A. Hudson, Anusha Balakrishnan, Varun Gangal, Dongyeop Kang, Tatsunori Hashimoto and Joel Tetreault

DistShift Workshop (Dec 13)


Organizers: Shiori Sagawa, Pang Wei Koh, Fanny Yang, Hongseok Namkoong, Jiashi Feng, Kate Saenko, Percy Liang, Sarah Bird and Sergey Levine

Data-centric AI Workshop (Dec 14)


Organizers: Andrew Ng, Lora Aroyo, Cody Coleman, Greg Diamos, Vijay Janapa Reddi, Joaquin Vanschoren,Carole-Jean Wu and Sharon Zhou

Physical Reasoning and Inductive Biases for the Real World Workshop (Dec 14)


Organizers: Krishna Murthy Jatavallabhula, Rika Antonova, Kevin Smith, Hsiao-Yu (Fish) Tung, Florian Shkurti, Jeannette Bohg and Josh Tenenbaum

Workshop Papers

  • How Does Contrastive Pre-training Connect Disparate Domains? by Kendrick Shen*, Robbie Jones*, Ananya Kumar*, Sang Michael Xie*, Percy Liang (DistShift Workshop)
  • Optimal Representations for Covariate Shifts by Yann Dubois, Yangjun Ruan, Chris J. Maddison (DistShift Workshop)
  • Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations by Michael Zhang, Nimit S. Sohoni, Hongyang R. Zhang, Chelsea Finn, Christopher Ré (DistShift Workshop)
  • Extending the WILDS Benchmark for Unsupervised Adaptation by Shiori Sagawa*, Pang Wei Koh*, Tony Lee*, Irena Gao*, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang (DistShift Workshop)
  • Calibrated Ensembles: A Simple Way to Mitigate ID-OOD Accuracy Tradeoffs by Ananya Kumar, Aditi Raghunathan, Tengyu Ma, Percy Liang (DistShift Workshop)
  • Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective by Margalit Glasgow*, Honglin Yuan*, Tengyu Ma (New Frontiers in Federated Learning)
  • What Matters in Learning from Offline Human Demonstrations for Robot Manipulation | Blog Post | Video | Website by Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, Roberto Martín-Martín (Offline Reinforcement Learning Workshop)
  • An Algorithmic Theory of Metacognition in Minds and Machines | Blog Post by Rylan Schaeffer (Metacognition in the Age of AI: Challenges and Opportunities)
  • Beyond Ads: Sequential Decision-Making Algorithms in Public Policy by Peter Henderson, Ben Chugg, Brandon Anderson, Daniel E. Ho (Workshop on Causal Inference Challenges in Sequential Decision Making)
  • Tracking Urbanization in Developing Regions withRemote Sensing Spatial-Temporal Super-Resolution by Yutong He*, William Zhang*, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon (Workshop on Machine Learning for the Developing World (ML4D))
  • Likelihood-free Density Ratio Acquisition Functions are not Equivalent to Expected Improvements by Jiaming Song, Stefano Ermon (Bayesian Deep Learning Workshop)
  • Exploiting Proximity Search and Easy Examples to Select Rare Events by Daniel Kang, Alex Derhacobian, Kaoru Tsuji, Trevor Hebert, Peter Bailis, Tadashi Fukami, Tatsunori Hashimoto, Yi Sun, Matei Zaharia (Data Centric AI workshop)

We look forward to seeing you at NeurIPS 2021!