Selected MIT-IBM Watson AI Lab Publications

The publications listed below are co-authored by MIT and IBM researchers.


Association for the Advancement of Artificial Intelligence (AAAI) 2019

Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems
Trong Nghia Hoang (MIT), Quang Minh Hoang, Kian Hsiang Low, Jonathan How (MIT)
Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation
Lijie Fan (MIT), Wenbing Huang, Chuang Gan (IBM), Junzhou Huang, Boqing Gong
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
Akhilan Boopathy (MIT), Tsui-Wei Weng (MIT), Pin-Yu Chen (MIT-IBM Lab), Sijia Liu (MIT-IBM Lab), Luca Daniel (MIT)
Learning to Teach in Cooperative Multiagent Reinforcement Learning
Shayegan Omidshafiei (MIT), Dong-Ki Kim (MIT), Miao Liu (IBM), Gerald Tesauro (IBM), Matthew Riemer (MIT-IBM Lab), Christopher Amato, Murray Campbell (IBM), Jonathan P. How (MIT)
MIT News Article


Artificial Intelligence and Statistics (AISTATS) 2019

ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
Raj Agrawal (MIT), Karthikeyan Shanmugam (MIT-IBM Lab), Chandler Squires (MIT), Caroline Uhler (MIT), Karren Yang (MIT)
Chordal Causal Structure Learning – link forthcoming
Kristjan Greenewald (MIT-IBM Lab), Murat Kocaoglu (IBM), Sara Magliacane (IBM), Chandler Squires (MIT), Guy Bresler (MIT)
Size of Interventional Markov Equivalence Classes in Random DAG Models
Dmitiry Katz (MIT-IBM Lab), Karthikeyan Shanmugam (MIT-IBM Lab), Chandler Squires (MIT), Caroline Uhler (MIT)


Cognitive Computational Neuroscience (CCN) 2019

A Computational Model for Combinatorial Generalization in Physical Auditory Perception
Yunyun Wang (MIT), Chuang Gan (MIT-IBM Lab), Max H. Siegel (MIT), Zhoutong Zhang(MIT), Jiajun Wu(MIT), Joshua B. Tenenbaum(MIT)


Computer Vision and Pattern Recognition (CVPR) 2019

Identifying Interpretable Action Concepts in Deep Networks
Kandan Ramakrishnan (MIT), Mathew Monfort (MIT), Barry A McNamara (MIT), Alex Lascelles (MIT), Aude Oliva (MIT), Dan Gutfreund (MIT-IBM Lab), Rogerio Feris (MIT-IBM Lab)
Grounding Spoken Words in Unlabeled Video
Angie Boggust (MIT), Kartik Audhkhasi (IBM), Dhiraj Joshi (IBM), David Harwath (MIT), Samuel Thomas (IBM), Rogerio Feris (IBM), Dan Gutfreund (IBM), Yang Zhang (IBM), Antonio Torralba (MIT), Michael Picheny (IBM), James Glass (MIT)


Empirical Methods in Natural Language Processing (EMNLP) 2019

Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control
Mo Yu (IBM), Shiyu Chang (IBM), Yang Zhang (IBM), Tommi Jaakkola (MIT)


International Conference on Computer Vision (ICCV) 2019

TSM: Temporal Shift Module for Efficient Video Understanding
Ji Lin (MIT), Chuang Gan (MIT-IBM Lab), Song Han (MIT)
The Sound of Motions
Hang Zhao (MIT), Chuang Gan (MIT-IBM Lab), Wei-Chiu Ma (MIT), Antonio Torralba (MIT)
MIT News Article
Seeing What a GAN Cannot Generate
David Bau (MIT), Jun-Yan Zhu (MIT), Jonas Wulff (MIT), William Peebles (MIT), Hendrik Strobelt (IBM), Bolei Zhou, Antonio Torralba (MIT)
Reasoning about Human-Object Interactions through Dual Attention Networks
Tete Xiao (IBM), Quanfu Fan (MIT-IBM Lab), Danny Gutfreund (MIT-IBM Lab), Bolei Zhou, Matthew Monfort (MIT), Aude Oliva (MIT)


International Conference on Learning Representations (ICLR) 2019

Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference
Matthew Riemer (MIT-IBM Lab), Ignacio Cases, Robert Ajemian (MIT), Miao Liu (MIT-IBM Lab), Irina Rish (MIT-IBM Lab), Yuhai Tu (MIT-IBM Lab), and Gerald Tesauro (MIT-IBM Lab)
ZDNet Article
Defensive Quantization: When Efficiency Meets Robustness
Ji Lin (MIT), Chuang Gan (MIT-IBM Lab), Song Han (MIT)
MIT News Article 
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
David Bau (MIT), Jun-Yan Zhu (MIT), Hendrik Strobelt (MIT-IBM Lab), Bolei Zhou, Joshua B. Tenenbaum (MIT), William T. Freeman (MIT), Antonio Torralba (MIT)
MIT Tech Review Article 
Learning Entropic Wasserstein Embeddings
Charlie Frogner (MIT), Farzaneh Mirzazadeh (MIT-IBM Lab), Justin Solomon (MIT)
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
Jiayuan Mao (MIT), Chuang Gan (MIT-IBM Lab), Pushmeet Kohli, Joshua B. Tenenbaum (MIT), Jiajun Wu (MIT)
MIT News Article 
MIT Tech Review Article


International Conference on Machine Learning (ICML) 2019

PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
Tsui-Wei Weng (MIT), Pin-Yu Chen (IBM), Lam M. Nguye (IBM), Mark S. Squillante (IBM), Ivan Oseledets, Luca Daniel (MIT)
Scalable Fair Clustering
Arturs Backurs, Piotr Indyk (MIT), Krzysztof Onak (MIT-IBM Lab), Baruch Schieber, Ali Vakilian (MIT), Tal Wagner(MIT)
Estimating Information Flow in Neural Networks
Ziv Goldfeld (MIT), Ewout van den Berg (MIT-IBM Lab), Kristjan Greenewald (MIT-IBM Lab), Igor Melnyk (MIT-IBM Lab), Nam Nguyen (MIT-IBM Lab), Brian Kingsbury (MIT-IBM Lab), Yury Polyanskiy (MIT)
Few-Shot Transfer Learning from Multiple Pre-Trained Networks – link forthcoming
Joshua Lee (MIT), Prasanna Sattigeri (IBM), Gregory Wornell (MIT)


International Joint Conference on Artificial Intelligence (IJCAI) 2019

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Aldo Pareja (MIT-IBM Lab), Giacomo Domeniconi (MIT-IBM Lab), Jie Chen (MIT-IBM Lab), Tengfei Ma (MIT-IBM Lab), Toyotaro Suzumura (MIT-IBM Lab), Hiroki Kanezashi (MIT-IBM Lab), Tim Kaler (MIT),Tao B. Schardl (MIT), Charles E. Leiserson (MIT)
Bayesian Inference of Linear Temporal Logic Specifications for Contrastive Explanations
Joseph Kim (MIT), Christian Muise (MIT-IBM Lab), Ankit Shah (MIT), Shubham Agarwal (MIT-IBM Lab), Julie Shah (MIT)
Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively
Serena Booth (MIT), Christian Muise (MIT-IBM Lab), Julie Shah (MIT)


IEEE International Symposium on Information Theory (ISIT) 2019

Convergence of Smoothed Empirical Measures with Applications to Entropy Estimation
Yury Polyanski (MIT), Ziv Goldfeld (MIT), Jonathan Weed (MIT), Kristjan Greenewald (MIT-IBM Lab)


North American Chapter of the Association for Computational Linguistics (NAACL) 2019

Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
Ethan Wilcox, Peng Qian (MIT), Richard Futrell, Miguel Ballesteros (MIT-IBM Lab), Roger Levy (MIT)
MIT News Article
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
Richard Futrell, Ethan Wilcox, Takashi Morita (MIT), Peng Qian (MIT), Miguel Ballesteros (MIT-IBM Lab), Roger Levy (MIT)
MIT News Article


Neural Information Processing Systems (NeurIPS) 2019

A Stratified Approach to Robustness for Randomly Smoothed Classifiers
Guang-He Lee (MIT), Yang Yuan (MIT), Shiyu Chang (IBM), Tommi Jaakkola (MIT)
A Game Theoretic Approach to Class-wise Selective Rationalization
Shiyu Chang (IBM), Yang Zhang (MIT-IBM Lab), Mo Yu (IBM), Tommi Jaakkola (MIT)
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks
Joshua Lee (MIT), Prasanna Sattigeri (IBM), Gregory Wornell (MIT)
Hierarchical Optimal Transport for Document Representation
Mikhail Yurochkin (MIT-IBM Lab), Sebastian Claici (MIT), Edward Chien (MIT), Farzaneh Mirzazadeh (MIT-IBM Lab), Justin M Solomon (MIT)
Alleviating Label Switching with Optimal Transport
Pierre Monteiller, Sebastian Claici (MIT), Edward Chien (MIT), Farzaneh Mirzazadeh (MIT-IBM Lab), Justin M Solomon (MIT), Mikhail Yurochkin (MIT-IBM Lab)
Sample Efficient Active Learning of Causal Trees
Kristjan Greenewald (IBM), Dmitriy Katz (IBM), Karthikeyan Shanmugam (IBM), Sara Magliacane (MIT-IBM Lab), Murat Kocaoglu (MIT-IBM Lab), Enric Boix Adsera (MIT), Guy Bresler (MIT)
Visual Concept-Metaconcept Learning
Chi Han, Jiayuan Mao (MIT), Chuang Gan (MIT-IBM Lab), Josh Tenenbaum (MIT), Jiajun Wu (MIT)
Objectnet: A Large-scale Bias-controlled Dataset for Pushing the Limits of Object Recognition Models
Andrei Barbu (MIT), David Mayo (MIT), Julian Alverio (MIT), William Luo (MIT), Christopher Wang (MIT), Dan Gutfreund (IBM), Josh Tenenbaum (MIT), Boris Katz (MIT)


ACM Special Interest Group on Computer Graphics (SIGGRAPH) 2019

GANPaint Studio: Semantic Photo Manipulation with a Generative Image Prior
David Bau (MIT), Hendrik Strobelt (MIT-IBM Lab), William Peebles (MIT), Jonas Wulff (MIT), Bolei Zhou, Jun-Yan Zhu (MIT), Antonio Torralba (MIT)


International Conference on Solid State Ionics (SSI) 2019

Controlling Conductive Filaments in Resistive Switching Oxides by Controlled Chemical Disorder – link forthcoming
Kevin J. May (MIT), Yu Ren Zhou (MIT), Takashi Ando(c), Vijay Narayanan (IBM), Harry L. Tuller (IBM), Bilge Yildiz (MIT)

Refereed Journal Publications 2019

Moments in Time Dataset: One Million Videos for Event Understanding
Mathew Monfort (MIT), Alex Andonian (MIT), Bolei Zhou, Kandan Ramakrishnan (MIT), Sarah Adel Bargal, Tom Yan (MIT), Lisa Brown (IBM), Quanfu Fan (IBM), Dan Gutfruend (IBM), Carl Vondrick, Aude Oliva (MIT)
Experiences and Insights for Collaborative Industry-Academic Research in Artificial Intelligence
Lisa Amini (IBM), Ching-Hua Chen (IBM), David Cox (IBM), Aude Oliva (MIT-IBM Lab), Antonio Torralba (MIT-IBM Lab)
AI Magazine (in press)
“Inactive” Ingredients in Oral Medications
Daniel Reker (MIT-IBM Lab), Steven M. Blum (MIT), Christoph Steiger (MIT-IBM Lab), Kevin E. Anger, Jamie M. Sommer, John Fanikos, Giovanni Traverso (MIT-IBM Lab)
Science Translational Medicine
Supervised Learning With Quantum-enhanced Feature Spaces 
Vojtěch Havlíček (IBM), Antonio D. Córcoles (IBM), Kristan Temme (IBM), Aram W. Harrow (MIT), Abhinav Kandala (IBM), Jerry M. Chow (IBM), Jay M. Gambetta (IBM)
Nature 567, 209–212


Association for Computational Linguistics (ACL) 2018

Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning
Hongge Chen (MIT), Huan Zhang (IBM), Pin-Yu Chen (IBM), Jinfeng Yi, Cho-Jui Hsieh


Computer Vision and Pattern Recognition (CVPR) 2018

Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
Xiang Long, Chuang Gan (MIT-IBM Lab), Gerard de Melo, Jiajun Wu (MIT), Xiao Liu, Shilei Wen
Sparse, Smart Contours to Represent and Edit Images
Tali Dekel, Chuang Gan (MIT-IBM Lab), Dilip Krishnan, Ce Liu, William T. Freeman (MIT)


European Conference on Computer Vision (ECCV) 2018

The Sound of Pixels
Hang Zhao (MIT), Chuang Gan (MIT-IBM Lab), Andrew Rouditchenko (MIT), Carl Vondrick, Josh McDermott (MIT), Antonio Torralba (MIT)
MIT News Article


Empirical Methods in Natural Language Processing (EMNLP) 2018

Deriving Machine Attention from Human Rationales
Yujia Bao (MIT), Shiyu Chang (MIT-IBM Lab), Mo Yu (MIT-IBM Lab), Regina Barzilay (MIT)


International Conference on Learning Representations (ICLR) 2018

A Case Study on Neural Image Captioning
Tsui-Wei Weng (MIT), Huan Zhang, Pin-Yu Chen (MIT-IBM Lab), Jinfeng Yi, Dong Su (MIT-IBM Lab), Yupeng Gao (MIT-IBM Lab), Cho-Jui Hsieh, Luca Daniel (MIT)


International Conference on Machine Learning (ICML) 2018

Logical Rule Induction and Theory Learning Using Neural Theorem Proving
Andres Campero (MIT), Josh Tenenbaum (MIT), Aldo Pareja (MIT-IBM Lab), Tim Klinger (MIT-IBM Lab), Sebastian Riedel


Neural Information Processing Systems (NeurIPS) 2018

Scalable Graph Learning for Anti-Money Laundering: A First Look
Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl
NeurIPS Finance Workshop
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Kexin Yi, Jiajun Wu (MIT), Chuang Gan (MIT-IBM Lab), Antonio Torralba (MIT), Pushmeet Kohli, Joshua B. Tenenbaum (MIT)
Efficient Neural Network Robustness Certification with General Activation Functions
Huan Zhang, Tsui-Wei Weng (MIT), Pin-Yu Chen (MIT-IBM Lab), Cho-Jui Hsieh, Luca Daniel (MIT)
Co-regularized Alignment for Unsupervised Domain Adaptation
Abhishek Kumar (MIT-IBM Lab), Prasanna Sattigeri (MIT-IBM Lab), Kahini Wadhawan (MIT-IBM Lab), Leonid Karlinsky (MIT-IBM Lab), Rogerio Feris (MIT-IBM Lab), William T. Freeman (MIT), Gregory Wornell (MIT)
Continual Learning with Self-Organizing Maps
Pouya Bashivan (MIT), Martin Schrimpf (MIT), Robert Ajemian (MIT), Irina Rish (MIT-IBM Lab), Matthew Riemer (MIT-IBM Lab), Yuhai Tu (MIT-IBM Lab)