New England Machine Learning Day 2018

About
Posters

The seventh annual New England Machine Learning Day will take place Monday, May 7, 2018, 10 AM–5 PM at Microsoft Research New England, One Memorial Drive, Cambridge, MA 02142. The event will bring together local academics and researchers in machine learning, artificial intelligence, and their application. There will be a lively poster session during lunch, followed by a provocative panel.

Also, consider joining a very worthwhile hackathon on June 11: New England Machine Learning for Accessibility and Neurodiversity.

Schedule

Time	Session
9:55am – 10:00am	Opening remarks
10:00am – 10:30am	Aleksander Mądry, MIT Towards ML You Can Rely On
10:35am – 11:05am	Alexandra Meliou, UMass Amherst Fairness testing: A systems’ perspective on algorithmic bias
11:10am – 11:40am	Vivienne Sze, MIT Energy-Efficient Deep Learning for Mobile Applications
11:40pm – 1:20pm	Lunch and posters
1:20pm – 2:15pm	Provocative Panel, Tina Eliassi-Rad (moderator) Panelists: Carla Brodley, Northeastern, Rania Khalaf, IBM, Michael Littman, Brown, Lester Mackey, MSR, and Josh Tenenbaum, MIT
2:20pm – 2:50pm	Daniel Ritchie, Brown Learning Procedural Modeling Programs for Computer Graphics from Examples
2:50pm – 3:20pm	Coffee break
3:20pm – 3:50pm	Kate Saenko, Boston University Adversarial Techniques for Visual Domain Adaptation
3:55pm – 4:25pm	Byron Wallace, Northeastern Training Neural NLP Models in Minimally Supervised Settings
4:30pm – 5:00pm	Lucas Janson, Harvard University Knockoffs: using machine learning for statistically-rigorous variable selection in nonparametric models

Organizing committee

Tina Eliassi-Rad, Northeastern
Pierre Jacob, Harvard
Adam Tauman Kalai, Microsoft Research (chair)
David Sontag, MIT

Poster chairs

Michael C. Hughes, postdoc, Harvard University
Christina Lee Yu, postdoc, Microsoft Research

Steering committee

Carla Brodley, Northeastern University
Adam Tauman Kalai, Microsoft Research
Joshua Tenenbaum, Massachusetts Institute of Technology
Alexander Rush, Harvard University

Related events

Poster Title	Presenting Author / Authors
Semi-Supervised Learning with Competitive Infection Models	Nir Rosenfeld, Harvard University/Amir Globerson, Tel Aviv University
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces	Daniel J Milstein, Brown University/ H.T. Kung, Harvard University; Jason L Pacheco, MITLeigh R Hochberg, Brown & MGH & VA & Harvard, John D Simeral, Brown & MGH & VABeata Jarosiewicz, NeuroPaceErik B Sudderth, UV Irvine & Brown
Breaking the n^{-1/2} barrier for permutation-based ranking models	Cheng Mao, MIT/Jonathan Weed, MIT; Philippe Rigollet, MIT; Ashwin Pananjady UC Berkeley; Martin J. Wainwright, UC Berkeley
Application of Breiman and Cutler’s Random Forest Algorithm for Identification of Mutated Genes Responsible for Drug Resistance in M. Tuberculosis Strains	Uma Girkar, MIT/ Ling TengHarvard Medical School; Dr. Gil Alterovitz, Harvard Medical School
Bayesian Nonparametrics in Julia	Vadim Smolyakov, MIT/ John W. Fisher III, MIT
An Epidemic Modeling Framework For Hashtag Diffusion on Congressional Twitter Networks	Cantay Caliskan, Boston University/ Dino P. Christenson, Boston University
Inference and Learning in Latent Count Models	Kevin Winner, UMass Amherst/Dan Sheldon, Professor, UMass Amherst, Mt. Holyoke College
Combating Imbalanced Data with Generative Adversarial Networks	Rheeya Uppaal, UMass Amherst
Stochastic dynamics of sensory cortical neurons underlie taste-related decision making	Narendra Mukherjee, Brandeis University/ Joseph Wachutka, Brandeis University; Donald B Katz, Brandeis University
Joint Event Detection and Description in Continuous Video Streams	Huijuan Xu, Boston University/ Boyang Li, Liulishuo Silicon Valley AI; LabVasili Ramanishka, Boston University; Leonid Sigal, University of British Columbia; Kate Saenko, Boston University
When Life Gives you Lemmas, Make an Cross-Document Event Coreference Resolution System	Chris Tanner, Brown University/Eugene Charniak, Brown University
Dissociating Linguistic Form and Meaning with Adversarial-Motivational Training	Alexey Romanov, University of Massachussetts/ Anna R., University of Massachusetts Lowell; Anna R., University of Massachusetts Lowell; David D., University of Massachusetts Lowell
Early Syntactic Bootstrapping in an Incremental Memory-Limited Word Learner	Sepideh Sadeghi, Tufts University/ Sepideh S., Tufts University; Matthias S., Tufts University
Synthetic and Natural Noise Both Break Neural Machine Translation	Yonatan Belinkov, MIT/ Yonatan Bisk, University of Washington
Unbiased Hamiltonian Monte Carlo with couplings	Jeremy Heng, Harvard University/ Pierre Jacob, Harvard University
State Abstractions for Lifelong Reinforcement Learning	David Abel, Brown University/ Dilip Arumugam, Brown University; Lucas Lehnert, Brown University; Michael L. Littman, Brown University
Policy and Value Transfer for Lifelong Reinforcement Learning	Yuu Jinnai, Brown University/ David Abel, Brown University; George Konidaris, Brown University; Michael Littman, Brown University; Yue Gao, Brown University
A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric	Ruidi Chen, Boston University/ Ioannis Ch. Paschalidis, Boston University
Generalizing Bottleneck Problems	Hsiang Hsu, Harvard University/ Shahab Asoodeh, University of Chicago; Salman Salamatian, MIT; Flavio P. Calmon, Harvard University
Limits of Learning to Reduce Incompleteness in Partially Observed Networks	Timothy LaRock, Northeastern University/ Sahely Bhadra, Indian Institute of Technology; Tina Eliassi-Rad, Northeastern University
Distributing Frank-Wolfe via Map-Reduce	Armin Moharrer, Northeastern University/ Stratis Ioannidis, Northeastern University
Non-Parametric Inference for Gaussian Process	Linfeng Liu, Tufts University/ Liping Liu., Tufts University
On the Sample Complexity of Adversarially Robust Generalization	Dimitris Tsipras, MIT/ Shibani Santurkar, MIT; Ludwig Schmidt, MIT; Kunal Talwar, Google; Aleksander Madry, MIT
Optimality of Approximate Inference Algorithms on Stable Instances	Hunter Lang, MIT/ David Sontag, MIT; Aravindan Vijayaraghavan, Northwestern University
Graph Distance from the Topological Perspective of Nonbacktracking Cycles	Leo Torres, Northeastern University/ Tina Eliassi-Rad, Northeastern University
Correlation-based Time Series Analytics	Ramoza Ahsan, Worcester Polytechnic Institute/ Rodica Neamtu, Worcester Polytechnic Institute; Muzammil Bashir, Worcester Polytechnic Institute; Elke Rundensteiner, Worcester Polytechnic Institute; Garbor Sarkozy, Worcester Polytechnic Institute
Learning Deep Embeddings by Learning to Rank	Kun He, Boston University/ Fatih Cakir, First Fuel Software; Sarah Adel Bargal, Boston University; Stan Sclaroff, Boston University; Yan Lu, Amazon Lab126
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts	Sarthak Jain, Northeastern University/ Edward Banner, CCIS, Northeastern University; Jan-Willem van de Meent, Northeastern University; Iain J Marshall, King’s College London; Byron C Wallace, CCIS, Northeastern University
Time Series Analysis via Matrix Estimation	Anish Agarwal, MIT/ Muhammad Jehangir Amjad, MIT; Devavrat Shah, MIT; Dennis Shen, MIT
Why did they cite that?	Charles Lovering, Worcester Polytechnic Institute/ Jake Whitehill, WPI
Committee-Based Anomaly Detection with Explanations	Leilani H. Gilpin, MIT/ Gerald Jay Sussman, MIT
Multiagent Norm Identification: A Belief-Theoretic Approach for Automatically Identifying Explicitly Represented Norms from Observation	Vasanth Sarathy, Tufts University/ Matthias Scheutz, Tufts University
Improving Emotion Detection with Sub-clip Classification Boosting	Ermal Toto, Worcester Polytechnic Institute/ Brandon F. WPI; Elke R., WPI
Distributionally Robust Submodular Maximization	Matthew Staib, MIT/ Bryan Wilder, USC; Stefanie Jegelka, MIT
Experimental Design under Bradley Terry Model	Yuan Guo, Northeastern University/ Peng Tian Northeastern University; Jayashree Kalpathy-Cramer, Harvard Medical School; Susan Ostmo, Oregon Health & Science University; J. Peter Campbell, Oregon Health & Science University; Michael F.Chiang, Oregon Health & Science University; Deniz Erdogmus, Northeastern University; Jennifer Dy, Northeastern University; Stratis Ioannidis, Northeastern University
Deep Learning for Optimal Filtering	Matt Weiss, Worcester Polytechnic Institute/ Randy C. Paffenroth, Worcester Polytechnic Institute; Joshua R. Uzarski, U.S. Army NSRDEC; Jacob R. Whitehill, Worcester Polytechnic Institute
Separation of time scales and direct computation of weights in deep neural networks	Nima Dehmamy, Northeastern University/ Neda Rohani, Northwestern University; Aggelos Katsaggelos, Northwestern University
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks	Pu Zhao, Northeastern University/ Sijia Liu, IBM research; AIKaidi Xu, Northeastern University; Yanzhi Wang, Northeastern University; Xue Lin, Northeastern University
Improving Shape Deformation in Unsupervised Image-to-Image Translation	Aaron Gokaslan, Brown University/ Vivek Ramanujan, Brown University; Daniel Ritchie, Brown University; Kwang In Kim, University of Bath; James Tompkin, Brown University
Learning in POMDPs with Monte Carlo Tree Search	Sammie Katt, Northeastern University/ Frans A. Oliehoek, University of Liverpool; Christopher Amato, Northeastern University
Learning to Place Objects: A Network-based Approach	Xindi Wang, Northeastern University/ Onur Varol, Northeastern University; Tina Eliassi-Rad, Northeastern University; Albert-László Barabási, Northeastern University
Practical Data-Dependent Metric Compression with Provable Guarantees	Tal Wagner, MIT/ Piotr Indyk, MIT; Ilya Razenshteyn, Microsoft Research
Hierarchical Disentangled Representations	Babak Esmaeili, Northeastern University/ Hao Wu., Northeastern University; Sarthak Jain., Northeastern University; N. Siddhart., University of Oxford; Brooks Paige., University of Cambridge; Jan-Willem Van de Meent., Northeastern University
On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning	Hao Wang, Harvard University/ Berk Ustun, Harvard University; Flavio P. Calmon, Harvard University
One-shot Learning and Classification in Kids	Eliza Kosoy, MIT/ Brenden Lake, NYU; Laura Schulz, MIT; Joshua Tenenbaum, MIT
ShrinkNets: Learning Network Size while Training	Guillaume Leclerc, MIT/ Raul, Castro MIT; Manasi Vartak, MIT; Sam Madden, MIT; Tim Kraska, MIT
Low Variance Gradients for Variational Inference	Tomas Geffner, University of Massachussetts Amherst/ Justin Domke., University of Massachusetts Amherst
Topically-Coherent Neural Language Model Conditioned on Arbitrary Features	Xiao Qin, Worcester Polytechnic Institute/ Elke Rundensteiner, Worcester Polytechnic Institute; Xiangnan Kong, Worcester Polytechnic Institute
Inferring Electrification in Developing Nations via Hierarchical Beta Models of Multimodal Observations	Christopher L. Dean, MIT/ Stephen J. Lee, Massachusetts Institute of Technology John W. Fisher III, Massachusetts Institute of Technology
Automated software vulnerability detection using Long Short-Term Memory	Lei Hamilton, Draper/ Lei H. Hamilton, Draper; Jacob A. Harer, Draper/Boston University; Louis Y. Kim, Draper; Rebecca L. Russell, Draper; Onur Ozdemir, Draper; Leonard R. Kosta, Draper/Boston University; Akshay Rangamani, John Hopkins University; Gabriel I. Centeno, Draper/Northeasten University; Jonathan R. Key, Draper; Paul M. Ellingwood, Draper; Marc W. McConley, Draper; Jeffrey M. Opper, Draper; Peter Chin, Boston University; Tomo Lazovich, Draper