New England Machine Learning Day 2018

New England Machine Learning Day 2018


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.


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

Poster chairs

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