Microsoft at NeurIPS 2020

Microsoft at NeurIPS 2020

About

Microsoft is delighted to sponsor and attend the 34th Annual Conference on Neural Information Processing System (NeurIPS 2020), the largest machine learning conference of the year. As a proud Platinum Sponsor of NeurIPS 2020, Microsoft will showcase state-of-the-art research with over 100 co-authored papers, as well as participation in a variety of workshops, tutorials and competitions. If you are attending NeurIPS 2020, we encourage you to stop by our virtual booth to chat with our experts, see demos of our latest research, and find out about career opportunities with Microsoft. In addition to our participation in the main conference, Microsoft is also proud to sponsor and participate in the Black in AI, Queer in AI, and Women in Machine Learning Workshops.

NeurIPS 2020 Invited talk

Watch Chris Bishop‘s Posner Lecture
The Real AI Revolution

Virtual Fireside Chat with Professor Yoshua Bengio & Dr. Chris Bishop

Organizing Committee members

Danielle Belgrave, Tutorial Co-Chair
Katja Hofmann, Demonstration and Competitions Co-Chair
Yale Song, Expo Co-Chair
Lester Mackey, Diversity and Inclusion Co-Chair

NeurIPS Foundation Board 2020

Hanna Wallach, Board Member

Booth schedule

Live chat with us

Join us in the Microsoft booth to chat with experts about our research and open opportunities with Microsoft. See below for our live chat and live demo schedules.

Sunday, December 6
Time (PT) Name, Title
19:00–20:00 Vishak Gopal, Principal Software Engineer
Monday, December 7
Time (PT) Name, Title
13:00–14:00 Edward Tiong, Data & Applied Scientist
14:00–15:00 Akshay Krishnamurthy, Researcher
Vikas Gosain, HR Team/Recruiter
15:00–16:00 Alekh Agarwal, Researcher
Vikas Gosain, HR Team/Recruiter
16:00–17:00 Jason Eisner, Researcher
Tuesday, December 8
Time (PT) Name, Title
04:00–05:00 Cammy Vasquez, HR Team
05:00–06:00 Hannes Schulz, Senior Researcher
11:00–12:00 Akshay Krishnamurthy, Researcher
Subho Mukherjee, Senior Scientist
Vikas Gosain, HR Team/Recruiter
12:00–13:00 Arushi Jain, Data & Applied Scientist
Kevin Yang, Researcher
Paul Mineiro, Research & Engineering
13:00–14:00 Amy Siebenthaler, HR Team/Recruiter
Andrea Trevino Gavito, Data & Applied Scientist
Praveen Palanisamy, Research & Engineering
14:00–15:00 Amy Siebenthaler, HR Team/Recruiter
Jenna Hong, Data & Applied Scientist
15:00–16:00 Tristan Naumann, Researcher
Vikas Gosain, HR Team/Recruiter
16:00–17:00 Ahmed Awadallah, Principal Research Manager
Chi Wang, Researcher
Jason Eisner, Researcher
Wednesday, December 9
Time (PT) Name, Title
04:00–05:00 Hannes Schulz, Senior Researcher
Harm van Seijen, Researcher
11:00–12:00 Amy Siebenthaler, HR Team/Recruiter
Andrea Trevino Gavito, Data & Applied Scientist
James Budnik, HR Team/Recruiter
Jenna Hong, Data & Applied Scientist
12:00–13:00 Paul Mineiro, Research & Engineering
Dipendra Misra, Researcher
13:00–14:00 Amy Siebenthaler, HR Team/Recruiter
Vishak Gopal, Principal Software Engineer
14:00–15:00 Arushi Jain, Data & Applied Scientist
Emre Kıcıman, Researcher
Ossie Roycroft, HR Team/Recruiter
15:00–16:00 Hari Dubey, Scientist/Engineer
Vikas Gosain, HR Team/Recruiter
16:00–17:00 Alekh Agarwal, Researcher
Vikas Gosain, HR Team/Recruiter
Thursday, December 10
Time (PT) Name, Title
04:00–05:00 Cammy Vasquez, HR Team
Hannes Schulz, Senior Researcher
05:00–06:00 Harm van Seijen, Researcher
Jenna Hong, Data & Applied Scientist
06:00–07:00 Cassandra Oduola, Responsible AI
09:00–10:00 Women at Microsoft
Andrea Trevino Gavito, Microsoft AI Development
Anusua Trivedi, AI for Good
Arushi Jain, Microsoft AI Development
Besmira Nushi, Responsible AI, Human AI-Collaboration
Cassandra Oduola, Responsible AI
Evelyne Viegas, Research Engagement Outreach Programs (e.g., collaborative projects, fellowships)
Fanny Nina Paravecino, CAST (Cloud AI System Technology in Azure)
Hanna Wallach, FATE (fairness, accountability, transparency, and ethics in AI)
Jenn Wortman Vaughan, FATE (fairness, accountability, transparency, and ethics in AI)
Jenna Hong, Microsoft AI Development
Katja Hofmann, Game Intelligence
Lingling Zheng, AI Ops/Cloud Intelligence
Sarah Bird, Responsible AI Lead, Azure Cognitive Services
Shruthi Bannur, Health Intelligence
11:00–12:00 Amy Siebenthaler, HR Team/Recruiter
Andrea Trevino Gavito, Data & Applied Scientist
Jenna Hong, Data & Applied Scientist
12:00–13:00 Paul Mineiro, Research & Engineering
Remi Tachet, Senior Researcher
13:00–14:00 Amy Siebenthaler, HR Team/Recruiter
Dipendra Misra, Researcher
Jenna Hong, Data & Applied Scientist
14:00–15:00 Arushi Jain, Data & Applied Scientist
Ossie Roycroft, HR Team/Recruiter
15:00–16:00 Urszula Chajewska, Data & Applied Scientist
Vikas Gosain, HR Team/Recruiter
16:00–17:00 Jason Eisner, Researcher
Vikas Gosain, HR Team/Recruiter

Live Demo schedule

Tuesday, December 8
Time (PT) Live Demo
11:00–12:00 MineRL, Sean Kuno
13:00–13:30 Recruiting Q&A, Amy Sibenthaler
15:00–16:00 MineRL, Sean Kuno
Wednesday, December 9
Time (PT) Live Demo
11:00–12:00 MineRL, Sean Kuno
13:00–13:30 Recruiting Q&A, Amy Sibenthaler
15:00–16:00 MineRL, Sean Kuno
17:00–18:00 The future of intelligent sensors and Low code/No code model creation, Henry Jerez
Thursday, December 10
Time (PT) Live Demo
11:00–12:00 MineRL, Sean Kuno
13:00–13:30 Recruiting Q&A, Amy Sibenthaler
15:00–16:00 MineRL, Sean Kuno
16:00–17:00 The future of intelligent sensors and Low code/No code model creation, Henry Jerez

Accepted papers

Monday, December 7

Monday, December 7

18:15–18:30 PT | Oral: COVID/Health/Bio Applications

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement

Xin Liu, Josh Fromm, Shwetak Patel, Daniel McDuff

Poster Session: December 7

19:00–19:10 PT | Spotlight: Representation/Relational

On the Equivalence between Online and Private Learnability beyond Binary Classification

Young H Jung, Baekjin Kim, Ambuj Tewari

Poster Session: December 7

20:20–20:30 PT | Spotlight: Reinforcement Learning

Safe Reinforcement Learning via Curriculum Induction

Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal

Poster Session: December 7


21:00–23:00 PT | Poster Session 0

Adversarial Attacks on Deep Graph Matching

Zijie Zhang, Zeru Zhang, Yang Zhou, Yelong Shen, Ruoming Jin, Dejing Dou

Constrained episodic reinforcement learning in concave-convex and knapsack settings

Kianté Brantley, Miro Dudik, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun

Hierarchical Poset Decoding for Compositional Generalization in Language

Yinuo Guo, Zeqi Lin, Jian-Guang Lou, Dongmei Zhang

Information Theoretic Regret Bounds for Online Nonlinear Control

Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun

Learning Dynamic Belief Graphs to Generalize on Text-Based Games

Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikuláš Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, Will Hamilton

MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, Ming Zhou

MOReL: Model-Based Offline Reinforcement Learning

Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims

MPNet: Masked and Permuted Pre-training for Language Understanding

Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement

Xin Liu, Josh Fromm, Shwetak Patel, Daniel McDuff

Oral Session: December 7

On the Equivalence between Online and Private Learnability beyond Binary Classification

Young H Jung, Baekjin Kim, Ambuj Tewari

Spotlight Session: December 7

Parametric Instance Classification for Unsupervised Visual Feature learning

Yue Cao, Zhenda Xie, Bin Liu, Yutong Lin, Zheng Zhang, Han Hu

Provably Good Batch Reinforcement Learning Without Great Exploration

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

Restoring Negative Information in Few-Shot Object Detection

Yukuan Yang, Fangyun Wei, Miaojing Shi, Guoqi Li

Safe Reinforcement Learning via Curriculum Induction

Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal

Spotlight Session: December 7

Sampling-Decomposable Generative Adversarial Recommender

Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma, Xing Xie, Enhong Chen

Understanding Global Feature Contributions With Additive Importance Measures

Ian Covert, Scott Lundberg, Su-In Lee

Tuesday, December 8

Tuesday, December 8

06:30–06:45 PT | Oral: Reinforcement Learning

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs

Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun

Poster Session: December 8

07:00–07:10 PT | Spotlight: Social/Privacy

Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates

Wenhao Luo, Wen Sun, Ashish Kapoor

Poster Session: December 8

07:30–07:40 PT | Spotlight: Vision Applications

Learning Semantic-aware Normalization for Generative Adversarial Networks

Heliang Zheng, Jianlong Fu, Yanhong Zeng, Zheng-Jun Zha, Jiebo Luo

Poster Session: December 8

07:40–08:00 PT | Demonstration

MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval

Mark Hamilton, Lei Zhang, Bill Freeman, Marina Rogers, Darius Bopp, Johnny Bui, Margaret Wang, Mindren Lu, Zhenbang Chen, Christopher Hoder

08:20–08:30 PT | Spotlight: Reinforcement Learning

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs

Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu

Poster Session: December 8


09:00–11:00 PT | Poster Session 1

CoinPress: Practical Private Mean and Covariance Estimation

Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan Ullman

Cross-validation Confidence Intervals for Test Error

Pierre Bayle, Alexandre Bayle, Lucas Janson, Lester Mackey

Deep Reinforcement and InfoMax Learning

Bogdan Mazoure, Remi Tachet des Combes, Thang Long DOAN, Philip Bachman, R Devon Hjelm

Denoised Smoothing: A Provable Defense for Pretrained Classifiers

Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, J. Zico Kolter

Efficient Contextual Bandits with Continuous Actions

Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins

Fairness in Streaming Submodular Maximization: Algorithms and Hardness

Marwa El Halabi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs

Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun

Oral Session: December 8

Learning Semantic-aware Normalization for Generative Adversarial Networks

Heliang Zheng, Jianlong Fu, Yanhong Zeng, Zheng-Jun Zha, Jiebo Luo

Spotlight Session: December 8

Learning Structured Distributions From Untrusted Batches: Faster and Simpler

Sitan Chen, Jerry Li, Ankur Moitra

Learning the Linear Quadratic Regulator from Nonlinear Observations

Zakaria Mhammedi, Dylan Foster, Max Simchowitz, Wen Sun, Dipendra Misra, Akshay Krishnamurthy, Alexander Rakhlin, John Langford

Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates

Wenhao Luo, Wen Sun, Ashish Kapoor

Spotlight Session: December 8

Network size and size of the weights in memorization with two-layers neural networks

Sebastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer

On Infinite-Width Hypernetworks

Etai Littwin, Tomer Galanti, Lior Wolf, Greg Yang

PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning

Alekh Agarwal, Mikael Henaff, Sham Kakade, Wen Sun

Provably adaptive reinforcement learning in metric spaces

Tongyi Cao, Akshay Krishnamurthy

Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point

Bita Darvish Rouhani, Daniel Lo, Ritchie Zhao, Ming Liu, Jeremy Fowers, Kalin Ovtcharov, Anna Vinogradsky, Sarah Massengill, Lita Yang, Ray Bittner, Alessandro Forin, Haishan Zhu, Taesik Na, Prerak Patel, Shuai Che, Lok Chand Koppaka, Steve Reinhardt, Sitaram Lanka, Xia Song, Subhojit Som, Kaustav Das, Saurabh K T, Eric Chung, Doug Burger

RepPoints v2: Verification Meets Regression for Object Detection

Yihong Chen, Zheng Zhang, Yue Cao, Liwei Wang, Stephen Lin, Han Hu

Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization

Sam Hopkins, Jerry Li, Fred Zhang

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs

Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu

Spotlight Session: December 8

The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning

Harm Van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar


18:30–18:45 PT | Oral: Vision Applications

Do Adversarially Robust ImageNet Models Transfer Better?

Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry

Poster Session: December 8

19:30–19:40 PT | Spotlight: Vision Applications

Large-Scale Adversarial Training for Vision-and-Language Representation Learning

Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, Jingjing Liu

Poster Session: December 8

19:50–20:00 PT | Spotlight: Deep Learning/Theory

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

Arun Jambulapati, Jerry Li, Kevin Tian

Poster Session: December 8

20:50–20:00 PT | Spotlight: Reinforcement Learning

Policy Improvement via Imitation of Multiple Oracles

Ching-An Cheng, Andrey Kolobov, Alekh Agarwal

Poster Session: December 8


21:00–23:00 PT | Poster Session 2

Do Adversarially Robust ImageNet Models Transfer Better?

Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry

Oral Session: December 8

GreedyFool: Distortion-Aware Sparse Adversarial Attack

Xiaoyi Dong, Dongdong Chen, Jianmin Bao, Chuan Qin, Lu Yuan, Weiming Zhang, Nenghai Yu, Dong Chen

Large-Scale Adversarial Training for Vision-and-Language Representation Learning

Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, Jingjing Liu

Spotlight Session: December 8

Policy Improvement via Imitation of Multiple Oracles

Ching-An Cheng, Andrey Kolobov, Alekh Agarwal

Spotlight Session: December 8

RD2: Reward Decomposition with Representation Disentanglement

Zichuan Lin, Derek Yang, Li Zhao, Tao Qin, Guangwen Yang, Tie-Yan Liu

Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time

Jerry Li, Guanghao Ye

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

Arun Jambulapati, Jerry Li, Kevin Tian

Spotlight Session: December 8

Towards Interpretable Natural Language Understanding with Explanations as Latent Variables

Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, Jian Tang

Wednesday, December 9

Wednesday, December 9

06:15–06:30 PT | Oral: COVID/Applications/Composition

Learning Composable Energy Surrogates for PDE Order Reduction

Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan Adams

Poster Session: December 9

07:00–07:10 PT | Spotlight: COVID/Applications/Composition

Compositional Generalization by Learning Analytical Expressions

Qian Liu, Shengnan An, Jian-Guang Lou, Bei Chen, Zeqi Lin, Yan Gao, Bin Zhou, Nanning Zheng, Dongmei Zhang

Poster Session: December 9

07:30–07:40 PT | Spotlight: Continual/Meta/Misc Learning

Uncertainty-aware Self-training for Few-shot Text Classification

Subhabrata Mukherjee, Ahmed Awadallah

Poster Session: December 9

07:50–08:00 PT | Spotlight: Deep Learning

Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy

Edward Moroshko, Suriya Gunasekar, Blake Woodworth, Jason Lee, Nati Srebro, Daniel Soudry

Poster Session: December 9

08:10–08:20 PT | Spotlight: Optimization

Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms

Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli

Poster Session: December 9


09:00–11:00 PT | Poster Session 3

A Causal View on Robustness of Neural Networks

Cheng Zhang, Kun Zhang, Yingzhen Li

AvE: Assistance via Empowerment

Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel, Anca Dragan

BERT Loses Patience: Fast and Robust Inference with Early Exit

Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, Furu Wei

Compositional Generalization by Learning Analytical Expressions

Qian Liu, Shengnan An, Jian-Guang Lou, Bei Chen, Zeqi Lin, Yan Gao, Bin Zhou, Nanning Zheng, Dongmei Zhang

Spotlight Session: December 9

Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift

Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey Gordon

Efficient Algorithms for Device Placement of DNN Graph Operators

Jakub Tarnawski, Amar Phanishayee, Nikhil Devanur, Divya Mahajan, Fanny Nina Paravecino

Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games

Arun Suggala, Praneeth Netrapalli

Geometric Dataset Distances via Optimal Transport

David Alvarez Melis, Nicolo Fusi

How do fair decisions fare in long-term qualification?

Xueru Zhang, Ruibo Tu, Yang Liu, mingyan liu, Hedvig Kjellstrom, Kun Zhang, Cheng Zhang

Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy

Edward Moroshko, Suriya Gunasekar, Blake Woodworth, Jason Lee, Nati Srebro, Daniel Soudry

Spotlight Session: December 9

Learning Composable Energy Surrogates for PDE Order Reduction

Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan Adams

Oral Session: December 9

Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms

Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli

Spotlight Session: December 9

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang

Minimax Estimation of Conditional Moment Models

Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Chen Liu, Mathieu Salzmann, Tao LIN, Ryota Tomioka, Sabine Süsstrunk

Statistical Optimal Transport posed as Learning Kernel Embedding

Saketha Nath Jagarlapudi, Pratik Kumar Jawanpuria

The Pitfalls of Simplicity Bias in Neural Networks

Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli

Uncertainty-aware Self-training for Few-shot Text Classification

Subhabrata Mukherjee, Ahmed Awadallah

Spotlight Session: December 9

Zero-Resource Knowledge-Grounded Dialogue Generation

Linxiao Li, Can Xu, Wei Wu, Yufan Zhao, Xueliang Zhao, Chongyang Tao


19:10–19:20 PT | Spotlight: Graph/Meta Learning/Software

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang

Poster Session: December 9

20:10–20:20 PT | Spotlight: Graph/Meta Learning/Software

RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference

Oindrila Saha, Venkata Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain

Poster Session: December 9

20:20–20:30 PT | Spotlight: Vision Applications

RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder

Cheng Chi, Fangyun Wei, Han Hu

Poster Session: December 9


21:00–23:00 PT | Poster Session 4

A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices

Jiezhong Qiu, Chi Wang, Ben Liao, Richard Peng, Jie Tang

Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment

Govinda Kamath, Tavor Baharav, Ilan Shomorony

GAN Memory with No Forgetting

Chunyuan Li, Miaoyun Zhao, Jianqiao Li, Sijia Wang, Lawrence Carin

Online Influence Maximization under Linear Threshold Model

Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen

RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder

Cheng Chi, Fangyun Wei, Han Hu

Spotlight Session: December 9

RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference

Oindrila Saha, Venkata Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain

Spotlight Session: December 9

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang

Spotlight Session: December 9

Thursday, December 10

Thursday, December 10

07:10–07:20 PT | Spotlight: Optimization

Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method

Kiran Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

Poster Session: December 10

08:20–08:30 PT | Spotlight: Graph/Relational/Theory

Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples

Shafi Goldwasser, Adam Tauman Kalai, Yael Kalai, Omar Montasser

Poster Session: December 10


09:00–11:00 PT | Poster Session 5

Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples

Shafi Goldwasser, Adam Tauman Kalai, Yael Kalai, Omar Montasser

Spotlight Session: December 10

COPT: Coordinated Optimal Transport on Graphs

Yihe Dong, Will Sawin

Empirical Likelihood for Contextual Bandits

Paul Mineiro, Nikos Karampatziakis, John Langford

Implicit Regularization and Convergence for Weight Normalization

Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel Ward, Qiang Liu

On the Expressiveness of Approximate Inference in Bayesian Neural Networks

Andrew Foong, David Burt, Yingzhen Li, Richard E Turner

On Warm-Starting Neural Network Training

Jordan Ash, Ryan Adams

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

Chao Ma, Sebastian Tschiatschek, Richard E Turner, José Miguel Hernández-Lobato, Cheng Zhang


18:30–18:45 PT | Oral: Optimization

Fully Dynamic Algorithm for Constrained Submodular Optimization

Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakub Tarnawski, Morteza Zadimoghaddam

Poster Session: December 10


21:00–23:00 PT | Poster Session 6

Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping

Minjia Zhang, Yuxiong He

AdaTune: Adaptive Tensor Program Compilation Made Efficient

Menghao Li, Minjia Zhang, Chi Wang, Mingqin Li

Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search

Houwen Peng, Hao Du, Hongyuan Yu, QI LI, Jing Liao, Jianlong Fu

Disentangling Human Error from Ground Truth in Segmentation of Medical Images

Le Zhang, Ryutaro Tanno, Moucheng Xu, Chen Jin, Joseph Jacob, Olga Cicarrelli, Frederik Barkhof, Daniel Alexander

Fully Dynamic Algorithm for Constrained Submodular Optimization

Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakub Tarnawski, Morteza Zadimoghaddam

Oral Session: December 10

Graph Policy Network for Transferable Active Learning on Graphs

Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, Jian Tang

HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory

Jie Ren, Minjia Zhang, Dong Li

ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool

Gellert Weisz, András György, Wei-I Lin, Devon Graham, Kevin Leyton-Brown, Csaba Szepesvari, Brendan Lucier

Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks

Amir Rahimi, Amirreza Shaban, Ching-An Cheng, Richard I Hartley, Byron Boots

Passport-aware Normalization for Deep Model Protection

Jie Zhang, Dongdong Chen, Jing Liao, Weiming Zhang, Gang Hua, Nenghai Yu

Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method

Kiran Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

Spotlight Session: December 10

Semi-Supervised Neural Architecture Search

Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, Tie-Yan Liu

Stochastic Stein Discrepancies

Jackson Gorham, Anant Raj, Lester Mackey

Tutorials & Workshops

Sunday, December 6

10:00 PT | Workshop
Real World RL with Vowpal Wabbit: Beyond Contextual Bandits
Jacob Alber, John Langford, Rafah Hosn

14:00 PT | Talk
The Unpaved Path of Deploying Reliable and Human-Centered Machine Learning Systems
Besmira Nushi


Monday, December 7

06:00 – 12:30 PT | Workshop
Black in AI
Mentorship Roundtable Hosts: Danielle Belgrave, Hanna Wallach, Jenn Wortman Vaughan

08:00–10:30 PT | Tutorial
Advances in Approximate Inference
Yingzhen Li, Cheng Zhang


Tuesday, December 8

12:00–16:00 PT | Symposium
COVID-19 Symposium Day 1
Andrew Beam, Tristan Naumann, Katherine Heller, Elaine Nsoesie


Wednesday, December 9

01:40 – 18:00 PT | Workshop
Women in Machine Learning
Diversity & Inclusion Co-chair: Danielle Belgrave
Area Chair: Besmira Nushi
Mentorship Roundtable Hosts: Chris Bishop, Danielle Belgrave, Emma Pierson, Jenn Wortman Vaughan, John Langford, Kate Crawford, Nicolo Fusi, Sham Kakade, Stephanie Hyland, Susan Dumais

12:00–16:00 PT | Symposium
COVID-19 Symposium Day 2
Andrew Beam, Tristan Naumann, Katherine Heller, Elaine Nsoesie


Friday, December 11

06:50–16:50 PT | Workshop
Causal Discovery and Causality-Inspired Machine Learning
Biwei Huang, Sara Magliacane, Kun Zhang, Danielle Belgrave, Elias Bareinboim, Daniel Malinsky, Thomas Richardson, Christopher Meek, Peter Spirtes, Bernhard Schölkopf

06:00–16:20 PT | Workshop
Machine Learning for Health: Advancing Healthcare for All
Stephanie Hyland, Emily Alsentzer, Andrew Beam, Brett Beaulieu-Jones, Danielle Belgrave, Allen Schmaltz, Irene Y Chen, Anna Goldenberg, Matthew McDermott, Tristan Naumann, Charles Onu

08:30–21:00 PT | Workshop
ML Retrospectives, Surveys & meta-Analyses 
Chhavi Yadav, Prabhu Pradhan, Abhishek Gupta, Ryan Lowe, Peter Henderson, Jessica Forde Jessica Forde, Mayoore Jaiswal, Jesse Dodge


Saturday, December 12

Machine Learning For Systems | Workshop
Accepted paper: Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
Jayant Gupchup, Ashkan Aazami, Yaran Fan, Senja Filipi, Tom Finley, Scott Inglis, Marcus Asteborg, Luke Caroll, Rajan Chari, Markus Cozowicz, Vishak Gopal, Vinod Prakash, Sasikanth Bendapudi, Jack Gerrits, Eric Lau, Huazhou Liu, Marco Rossi, Dima Slobodianyk, Dmitri Birjukov, Matty Cooper, Nilesh Javar, Dmitriy Perednya, Sriram Srinivasan, John Langford, Ross Cutler, Johannes Gehrke

04:45–14:45 PT | Workshop
I Can’t Believe it is Not Better: Bridging the Gap between Theory and Empiricism in Probabilistic Machine Learning
Jessica Zosa Forde, Francisco Ruiz, Melanie F. Pradier, Aaron Schein, Finale Doshi-Velez, David Blei, Hanna Wallach

05:20–12:55 PT | Workshop
Cooperative AI
Thore Graepel, Dario Amodei, Vincent Conitzer, Allan Dafoe, Gillian Hadfield, Eric Horvitz, Sarit Kraus, Kate Larson, Yoram Bachrach

05:30–15:00 PT | Workshop
Navigating the Broader Impacts of AI Research
Accepted paper: Overcoming Failures of Imagination in AI Infused System Development and Deployment
Carolyn Ashurst, Rosie Campbell, Deborah Raji, Solon Barocas, Stuart Russell

06:00–15:00 PT | Workshop
Wordplay: When Language Meets Games
Prithviraj Ammanabrolu, Matthew HausknechtXingdi YuanMarc-Alexandre CôtéAdam Trischler, Kory Mathewson, John Urbanek, Jason Weston, Mark Riedl

08:50–18:40 PT | Workshop
Self-Supervised Learning – Theory and Practice
Accepted paper: Make Lead Bias in Your Favor: Zero-shot Abstractive News Summarization
Chenguang Zhu, Ziyi Yang, Robert Gmyr, Michael Zeng, Xuedong Huang

Competitions

Competitions

Diagnostic Questions: Predicting Student Responses and Measuring Question Quality
Simon Woodhead, Craig Barton, José Miguel Hernández-Lobato, Richard Turner, Jack Wang, Richard G. Baraniuk, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, Simon Peyton-JonesCheng Zhang

Efficient Open-Domain Question Answering
Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao
*First place in the Efficient Open-Domain Question Answering unrestrictive track

Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification with Clinical Time-series Data
James Jordon, Daniel Jarrett, Jinsung Yoon, Paul Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar, Nick Maxfield

Webinars

Discover more about work accepted at NeurIPS 2020 in these webinars presented by Microsoft Research. Join these experts as they present their research in practical tutorials and answer questions afterwards in a live Q&A. All presentations and live Q&A sessions are recorded and will be available on demand.

To learn more about the webinars and for registration, click “register” below. See our full list of offerings at the Microsoft Research Webinars homepage.


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