About
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 |
| 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
Posters
| 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
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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
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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
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David Abel, Brown University/ Dilip Arumugam, Brown University; Lucas Lehnert, Brown University; Michael L. Littman, Brown University |
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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
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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
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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 |
