Microsoft @ NeurIPS 2018

Microsoft @ NeurIPS 2018

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Posters


Tuesday, December 4, 2018


Adversarial Multiple Source Domain Adaptation
10:45 AM-12:45 PM | Room 210&230 AB #107

Han Zhao, Shanghang Zhang, Guanhang Wu, Jose M. F. Moura, Joao P. Costeira, Geoffrey Gordon

FRAGE: Frequency-Agnostic Word Representation
10:45 AM-12:45 PM | Room 210&230 AB #153

Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu

Frequency-Domain Dynamic Pruning for Convolutional Neural Networks
10:45 AM-12:45 PM | Room 210&230 AB #67

Zhenhua Liu, Jizheng Xu, Xiulian Peng, Ruiqin Xiong

Heterogeneous Bitwidth Binarization in Convolutional Neural Networks
10:45 AM-12:45 PM | Room 210&230 AB #69

Josh Fromm, Shwetak Patel, Matthai Philipose

Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices
10:45 AM-12:45 PM | Room 210&230 AB #72

Don Dennis, Chirag Pabbaraju, Harsha Vardhan Simhadri, Prateek Jain

Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models
10:45 AM-12:45 PM | Room 210&230 AB #73

Minjia Zhang, Xiaodong Liu, Wenhan Wang, Jianfeng Gao, Yuxiong He

On the Dimensionality of Word Embedding
10:45 AM-12:45 PM | Room 210&230 AB #110

Zi Yin, Yuanyuan Shen

The Lingering of Gradients: How to Reuse Gradients Over Time
10:45 AM-12:45 PM | Room 210&230 AB #2

Zeyuan Allen-Zhu

Towards Text Generation with Adversarially Learned Neural Outlines
10:45 AM-12:45 PM | Room 210&230 AB #14

Sandeep Subramanian, Sai Rajeswar Mudumba, Adam Trischler, Alessandro Sordoni, Aaron Courville, Chris Pal

A Dual Framework for Low-rank Tensor Completion
5:00 PM-7:00 PM | Room 210&230 AB #146

Madhav Nimishakavi, Bamdev Mishra, Pratik Kumar Jawanpuria

Bounded-Loss Private Prediction Markets
5:00 PM-7:00 PM | Room 210&230 AB #28

Rafael Frongillo, Bo Waggoner

Contamination Attacks in Multi-Party Machine Learning
5:00 PM-7:00 PM | Room 210&230 AB #158

Jamie Hayes, Olya Ohrimenko

Dialog-based Interactive Image Retrieval
5:00 PM-7:00 PM | Room 210&230 AB #55

Xiaoxiao Guo, Hui Wu, Yu Cheng, Steven Rennie, Rogerio Schmidt Feris

Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base
5:00 PM-7:00 PM | Room 210&230 AB #93

Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, Jian Yin

Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
5:00 PM-7:00 PM | Room 210&230 AB #94

Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett, Bill Dolan

Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation
5:00 PM-7:00 PM | Room 210&230 AB #85

Tianyu He, Tao Qin, Tie-Yan Liu, Yingce Xia, Xu Tan, Di He, Zhibo Chen

Local Differential Privacy for Evolving Data
5:00 PM-7:00 PM | Room 210&230 AB #153

Matthew Joseph, Aaron Roth, Jonathan Ullman, Bo Waggoner

Precision and Recall for Time Series
5:00 PM-7:00 PM | Room 210&230 AB #116

Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich

Supervising Unsupervised Learning
5:00 PM-7:00 PM | Room 210&230 AB #164

Vikas Garg, Adam Kalai

Turbo Learning for Captionbot and Drawingbot
5:00 PM-7:00 PM | Room 210&230 AB #54

Qiuyuan Huang, Pengchuan Zhang, Oliver Wu, Lei Zhang


Wednesday, December 5, 2018


Adversarial Text Generation via Feature-Mover’s Distance
10:45 AM-12:45 PM | Room 210&230 AB #129

Liqun Chen, Shuyang Dai, Chenyang Tao, Dinghan Shen, Zhe Gan, Haichao Zhang, Yizhe Zhang, Lawrence Carin

A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
10:45 AM-12:45 PM | Room 210&230 AB #159

Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu

Constructing Unrestricted Adversarial Examples with Generative Models
10:45 AM-12:45 PM | Room 210&230 AB #149

Yang Song, Rui Shu, Nate Kushman, Stefano Ermon

Global Non-convex Optimization with Discretized Diffusions
10:45 AM-12:45 PM | Room 210&230 AB #18

Murat A. Erdogdu, Lester Mackey, Ohad Shamir

Inexact trust-region algorithms on Riemannian manifolds
10:45 AM-12:45 PM | Room 210&230 AB #15

Hiroyuki Kasai, Bamdev Mishra

Is Q-Learning Provably Efficient?
10:45 AM-12:45 PM | Room 210&230 AB #165

Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael Jordon

M-Walk: Learning to Walk over Graphs with Monte Carlo Tree Search
10:45 AM-12:45 PM | Room 210&230 | AB #164

Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao

On Oracle-Efficient PAC RL with Rich Observations
10:45 AM-12:45 PM | Room 210&230 AB #111

Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert Schapire

On the Local Hessian in Back-propagation
10:45 AM-12:45 PM | Room 210&230 AB #43

Huishuai Zhang, Wei Chen, Tie-Yan Liu

Recurrent Transformer Networks for Semantic Correspondence
10:45 AM-12:45 PM | Room 210&230 AB #119

Seungryong Kim, Stephen Lin, SANG RYUL JEON, Dongbo Min, Kwanghoon Sohn

Universal Growth in Production Economies
10:45 AM-12:45 PM | Room 210&230 AB #72

Simina Branzei, Ruta Mehta, Noam Nisan

Weakly Supervised Dense Event Captioning in Videos
10:45 AM-12:45 PM | Room 210&230 AB #125

Xuguang Duan, Wenbing Huang, Chuang Gan, Jingdong Wang, Wenwu Zhu, Junzhou Huang

Natasha 2: Faster Non-Convex Optimization Than SGD
5:00 PM-7:00 PM | Room 210&230 AB #50

Zeyuan Allen-Zhu

Coupled Variational Bayes via Optimization Embedding
5:00 PM-7:00 PM | Room 210&230 AB #11

Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song

Dual Policy Iteration
5:00 PM-7:00 PM | Room 210&230 AB #124

Wen Sun, Geoffrey Gordon, Wen Sun, J. Andrew Bagnell

Probabilistic Matrix Factorization for Automated Machine Learning
5:00 PM-7:00 PM | Room 210&230 AB #15

Nicolo Fusi, Rishit Sheth, Melih Elibol

NEON2: Finding Local Minima via First-Order Oracles
5:00 PM-7:00 PM | Room 210&230 AB #45

Zeyuan Allen-Zhu, Yuanzhi Li

Teaching Inverse Reinforcement Learners via Features and Demonstrations
5:00 PM-7:00 PM | Room 210&230 AB #167

Luis Haug, Sebastian Tschiatschek, Adish Singla


Thursday, December 6, 2018


Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
10:45 AM-12:45 PM | Room 210&230 AB #165

Dylan Foster, Akshay Krishnamurthy

FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
10:45 AM – 12:45 PM | Room 210&230 AB #89

Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar,   Prateek Jain, Manik Varma

Gaussian Process Prior Variational Autoencoders
10:45 AM-12:45 PM | Room 210&230 AB #63

Nicolo Fusi, Luca Saglietti, Francesco Paolo Casale, Adrian Dalca, Jennifer Listgarten

Learning to Teach with Dynamic Loss Functions
10:45 AM-12:45 PM | Room 210&230 AB #155

Tao Qin, Tie-Yan Liu, Fei Tian, Yingce Xia, Lijun Wu, Yingce Xia, Lai Jian-Huang

Byzantine Stochastic Gradient Descent
10:45 AM-12:45 PM | Room 210&230 AB #164

Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li

Towards Deep Conversational Recommendations
10:45 AM-12:45 PM | Room 210&230 AB #118

Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz,Vincent Michalski, Laurent Charlin, Chris Pal

Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
5:00 PM-7:00 PM | Room 210&230 AB #15

Kevin Scaman, Francis Bach, Sebastien Bubeck, Yin Tat Lee, Laurent Massoulie

Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
5:00 PM-7:00 PM | Room 210&230 AB #63

Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli

Community Exploration: From Offline Optimization to Online Learning
5:00 PM-7:00 PM | Room 210&230 AB #153

Xiaowei Chen, Weiran Huang, Wei Chen, John C.S. Lui

Constrained Graph Variational Autoencoders for Molecule Design
5:00 PM-7:00 PM | Room 210&230 AB #103

Qi Liu, Miltos Allamanis, Marc Brockschmidt, Alexander Gaunt

How To Make the Gradients Small Stochastically
5:00 PM-7:00 PM | Room 210&230 AB #74

Zeyuan Allen-Zhu

Learning Beam Search Policies via Imitation Learning
5:00 PM-7:00 PM | Room 210&230 AB#104

Renato Negrinho, Matthew Gormley, Geoffrey Gordon

Learning SMaLL Predictors
5:00 PM-7:00 PM | Room 210&230 AB #98

Vikas K. Garg, Ofer Dekel, Lin Xiao

Neural Architecture Optimization
5:00 PM-7:00 PM | Room 210&230 AB #123

Renqian Luo, Fei Tian, Tao Qin, Enhon Chen, Tie-Yan Liu

Random Feature Stein Discrepancies
5:00 PM-7:00 PM | Room 210&230 AB #78

Jonathan Huggins, Lester Mackey


Spotlight Sessions

Local Differential Privacy for Evolving Data
Tuesday, December 4, 2018 | 3:35 PM–3:40 PM | Room 517CD

Matthew Joseph, Aaron Roth, Jonathan Ullman, Bo Waggoner

Bounded-Loss Private Prediction Markets
Tuesday, December 4, 2018 | 4:05 PM–4:10 PM | Room 517CD

Rafael Frongillo, Bo Waggoner

Precision and Recall for Time Series
Tuesday, December 4, 2018 | 4:50 PM–4:55 PM | Room 220CD

Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich

Supervising Unsupervised Learning
Tuesday, December 4, 2018 | 4:55 PM–5:00 PM | Room 517CD

Vikas Garg, Adam Kalai

A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
Wednesday, December 5, 2018 | 9:45 AM–9:50 AM | Room 220CD

Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu

Recurrent Transformer Networks for Semantic Correspondence
Wednesday, December 5, 2018 | 10:20 AM–10:25 AM | Room 220E

Seungryong Kim, Stephen Lin, Sang Ryul Jeon, Dongbo Min, Kwanghoon Sohn

On Oracle-Efficient PAC RL with Rich Observations
Wednesday, December 5, 2018 | 10:25 AM–10:30 AM | Room 220CD

Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert Schapire

Natasha 2: Faster Non-Convex Optimization Than SGD
Wednesday, December 5, 2018 | 4:40 PM–4:45 PM | Room 517CD

Zeyuan Allen-Zhu

Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
Thursday, December 6, 2018 | 4:55 PM–5:00 PM | Room 220CD

Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli

Oral Presentations

On the Dimensionality of Word Embedding
Thursday, December 6, 2018 | 10:05AM – 10:20AM | Room 220E

Zi Yin, Yuanyuan Shen

Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
Thursday, December 6, 2018 | 3:50 PM–4:05 PM | Room 517CD

Kevin Scaman, Francis Bach, Sebastien Bubeck, Laurent Massoulié, Yin Tat Lee

Workshops

2nd Workshop on Machine Learning on the Phone and other Consumer Devices (MLPCD 2)
Friday, December 7, 2018 | 8:00 AM-6:30 PM

Invited speaker: Manik Varma

MLSys: Workshop on Systems for ML and Open Source Software
Friday, December 7, 2018 | 8:00 AM-6:30 PM

Accepted paper: Matteo Interlandi, Sergiy Matusevych, Saeed Amizadeh, Shauheen Zahirazami, Markus Weimer | Machine Learning at Microsoft with ML.NET

Accepted paper: Gyeong-In Yu, Saeed Amizadeh, Byung-Gon Chun, Markus Weimer, Matteo Interlandi | Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach

Accepted paper: Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Markus Weimer | Coded Elastic Computing

Workshop on Security in Machine Learning
Friday, December 7, 2018 | 11:00 AM-1:30 PM

Keynote: danah boyd

Interpretability and Robustness in Audio, Speech, and Language
Saturday, December 8, 2018 | 9:00 AM-9:30 AM

Invited Speaker 1: Rich Caruana

Relational Representation Learning
Saturday, December 8, 2018 | 8:00 AM-6:30 PM

10:15 AM spotlight talk: Saeed Amizadeh | A Neural Framework for Learning DAG to DAG Translation

Privacy Preserving Machine Learning
Saturday, December 8, 2018 | 8:00 AM-6:30 PM

Poster: Bolin Ding, Janardhan Kulkarni and Sergey Yekhanin | A Distributed Algorithm For Differentially Private Heavy Hitters

Poster: Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olya Ohrimenko and Sergey Yekhanin | An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors

Second Workshop on Machine Learning for Creativity and Design
Saturday, December 8, 2018 | 8:00 AM-6:30 PM

Poster: Khyatti Gupta, Sonam Damani, Kedhar Nath Narahari | Using AI to Design Stone Jewelry

Wordplay: Reinforcement and Language Learning in Text-based Games
Saturday, December 8, 2018 | 8:00 AM-6:30 PM

Invited speaker: Katja Hofmann

Machine Learning Open Source Software 2018: Sustainable communities
Saturday, December 8, 2018 | 8:30 AM-5:35 PM

11:00AM poster: Mayank Meghwankshi, Pratik Jawanpuria, Anoop Kunchukuttan, Hiroyuki Kasai, Bamdev Michra, McTorch | a manifold optimization library for deep learning

11:00AM poster: Markus Weimer | Machine Learning at Microsoft with ML.NET

Demonstrations

Booth Demos

Come by our booth (#203) to see demos of our latest research. See schedule below:

Sunday, December 2

Time (EST) Demo
10:00 AM–10:30 AM Azure Machine Learning
12:30 PM–1:15 PM Azure Machine Learning
1:15 PM–2:00 PM Azure Machine Learning
4:00 PM–4:30 PM Azure Machine Learning
6:30 PM–7:30 PM Azure Machine Learning

Monday, December 3

Time (EST) Demo
10:30 AM–11:00 AM AI for Good
Azure Machine Learning
1:00 PM–1:45 PM Multi-Word Imput
AI for Good
Azure Machine Learning
1:45 PM–2:30 PM GesturePod
Infer.NET – Y. Zaykov
AI for Good
Azure Machine Learning
4:30 PM–5:00 PM AI for Good
Azure Machine Learning
6:30 PM–7:30 PM Azure Machine Learning
7:30 PM–8:30 PM Azure Machine Learning

Tuesday, December 4

Time (EST) Demo
9:40 AM–10:05 AM AI for Good
Azure Machine Learning
12:45 PM–1:30 PM Infer.NET – Y. Zaykov
AI for Good
Azure Machine Learning
1:30 PM–2:15 PM Multi-Word Imput
AI for Good
Azure Machine Learning
3:05 PM–3:30 PM AI for Good
Azure Machine Learning

Wednesday, December 5

Time (EST) Demo
9:20 AM–9:45 AM AI for Good
Azure Machine Learning
12:45 PM–1:30 PM GesturePod
Infer.NET – Y. Zaykov
AI for Good
Azure Machine Learning
1:30 PM–2:15 PM TextWorld
Ruuh
AI for Good
Azure Machine Learning
3:05 PM–3:30 PM Ruuh
AI for Good
Azure Machine Learning

Thursday, December 6

Time (EST) Demo
9:20 AM–9:45 AM AI for Good
Azure Machine Learning
12:45 PM–1:30 PM Multi-Word Imput
AI for Good
Azure Machine Learning
1:30 PM–2:15 PM GesturePod
Ruuh
AI for Good
Azure Machine Learning
3:05 PM–3:30 PM Infer.NET – Y. Zaykov
Ruuh
AI for Good
Azure Machine Learning

Friday, December 7

Time (EST) Demo
10:30 AM–11:00 AM Azure Machine Learning
12:00 PM–12:45 PM Azure Machine Learning
12:45 PM–1:30 PM GesturePod
Azure Machine Learning
1:30 PM–2:00 PM Azure Machine Learning
3:00 PM–3:30 PM Azure Machine Learning

Conference Demo Sessions

ML on Resource Constrained Edge Devices – GesturePod!
Sunday, December 2, 2018 | 2:00 PM–6:30 PM | Room 510ABCD

Shishir G. Patil, Don Kurian Dennis, Harsha Vardhan Simhadri, Prateek Jain

Ruuh: A Deep Learning Based Conversational Social Agent
Tuesday, December 4, 2018 | 10:45 AM-7:30 PM | Room 510ABCD | #D3

Manoj Chinnakotla, Kedhar Narahari, Nitya Raviprakash, Umang Gupta, Ankush Chatterjee, Sneha Magapu, Sonam Damani, Abhishek Mathur, Puneet Agrawal, Meghana Joshi, Khyatti Gupta

TextWorld: A Learning Environment for Text-based Games
Tuesday, December 4, 2018 | 10:45 AM-7:30 PM | Room 510ABCD | #D10

Eric Yuan, Wendy Tay, Marc-Alexandre Côté

Multi-Word Imputation and Sentence Expansion
Wednesday, December 5, 2018 | 10:45 AM-7:30 PM | Room 510ABCD | #D7

Douglas Orr, Osman Ramadan, Dmitry Stratiychuk, Błażej Czapp

Careers

Internship

AI Tooling
Computational Biology
Computer Vision (Healthcare)
Confidential Computing
Deep Learning and Natural Language Processing
Design
Formal Verification
Knowledge Technologies and Intelligent Experiences
Machine Learning
Machine Learning at Microsoft Research NYC
Machine Learning for Software Engineering
Program Synthesis
Software Engineer – Machine Learning
Software Engineering & Program Management
Research Software Engineer
Reinforcement Learning
Reinforcement Learning
Software Engineering
Spreadsheet Experience and Technology
Web Development

PhD Student & Recent Graduate

Microsoft AI Residency Program
Program Manager/Software Engineering
Software Engineering
Software Engineer – Machine Learning

Post-Doc

Post Doc Researcher – AI
Post Doc Researcher – Deep Learning
Post Doc Researcher – Designer / Creative Technologist
Post Doc Researcher – ML/AI
Post Doc Researcher – ML for Software Engineering
Post Doc Researcher – Optical Systems
Post Doc Researcher – Reinforcement Learning
Post Doc Researcher – Spreadsheets

Researcher

Principal Researcher – Machine Learning
Principal Researcher – Speech & Dialogue
Programming Language for Machine Learning
Researcher – AI
Researcher – Deep Learning
Researcher – Deep Learning
Researcher – Economics
Researcher – Reinforcement Learning
Researcher – RL
Researcher – RL
Senior Researcher – Speech & Dialogue

Engineer/Scientist

Cloud Software Engineer
Data & Applied Scientist – Machine Learning
Data Scientist 2
Machine Learning/AI Engineer
Senior Applied Scientist – Speech Recognition
Senior/Principal Software Engineer – Blockchain
Senior Software Engineer – Security
Senior Software Engineer – Systems
Software Engineer
Software Engineer 2
Software Engineer 2
Software Engineer – Acceleration for ML Workloads
Software Engineer – Machine Learning
Software Engineer 2 – Machine Teaching
Software Engineer – Security

Analytics

NeurIPS Conference Analytics

Microsoft Academic | November 23, 2018

The Microsoft Academic Graph makes it possible to gain analytic insights about any of the entities within it: publications, authors, institutions, topics, journals, and conferences.