Poster Title | Presenting Author / Authors |
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Semi-Supervised Learning with Competitive Infection Models
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Nir Rosenfeld, Harvard University/Amir Globerson, Tel Aviv University
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Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces
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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
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Application of Breiman and Cutler’s Random Forest Algorithm for Identification of Mutated Genes Responsible for Drug Resistance in M. Tuberculosis Strains
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Uma Girkar, MIT/ Ling TengHarvard Medical School; Dr. Gil Alterovitz, Harvard Medical School |
Bayesian Nonparametrics in Julia
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Vadim Smolyakov, MIT/ John W. Fisher III, MIT |
An Epidemic Modeling Framework For Hashtag Diffusion on Congressional Twitter Networks
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Cantay Caliskan, Boston University/ Dino P. Christenson, Boston University |
Inference and Learning in Latent Count Models
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Kevin Winner, UMass Amherst/Dan Sheldon, Professor, UMass Amherst, Mt. Holyoke College |
Combating Imbalanced Data with Generative Adversarial Networks
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Rheeya Uppaal, UMass Amherst
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Stochastic dynamics of sensory cortical neurons underlie taste-related decision making
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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
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Chris Tanner, Brown University/Eugene Charniak, Brown University |
Dissociating Linguistic Form and Meaning with Adversarial-Motivational Training
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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
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Sepideh Sadeghi, Tufts University/ Sepideh S., Tufts University; Matthias S., Tufts University |
Synthetic and Natural Noise Both Break Neural Machine Translation
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Yonatan Belinkov, MIT/ Yonatan Bisk, University of Washington |
Unbiased Hamiltonian Monte Carlo with couplings
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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 |
Policy and Value Transfer for Lifelong Reinforcement Learning
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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
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Ruidi Chen, Boston University/ Ioannis Ch. Paschalidis, Boston University
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Generalizing Bottleneck Problems
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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
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Armin Moharrer, Northeastern University/ Stratis Ioannidis, Northeastern University |
Non-Parametric Inference for Gaussian Process
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Linfeng Liu, Tufts University/ Liping Liu., Tufts University |
On the Sample Complexity of Adversarially Robust Generalization
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Dimitris Tsipras, MIT/ Shibani Santurkar, MIT; Ludwig Schmidt, MIT; Kunal Talwar, Google; Aleksander Madry, MIT |
Optimality of Approximate Inference Algorithms on Stable Instances
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Hunter Lang, MIT/ David Sontag, MIT; Aravindan Vijayaraghavan, Northwestern University
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Graph Distance from the Topological Perspective of Nonbacktracking Cycles
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Leo Torres, Northeastern University/ Tina Eliassi-Rad, Northeastern University |
Correlation-based Time Series Analytics
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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
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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
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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
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Anish Agarwal, MIT/ Muhammad Jehangir Amjad, MIT; Devavrat Shah, MIT; Dennis Shen, MIT |
Why did they cite that?
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Charles Lovering, Worcester Polytechnic Institute/ Jake Whitehill, WPI |
Committee-Based Anomaly Detection with Explanations
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Leilani H. Gilpin, MIT/ Gerald Jay Sussman, MIT |
Multiagent Norm Identification: A Belief-Theoretic Approach for Automatically Identifying Explicitly Represented Norms from Observation
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Vasanth Sarathy, Tufts University/ Matthias Scheutz, Tufts University |
Improving Emotion Detection with Sub-clip Classification Boosting
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Ermal Toto, Worcester Polytechnic Institute/ Brandon F. WPI; Elke R., WPI |
Distributionally Robust Submodular Maximization
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Matthew Staib, MIT/ Bryan Wilder, USC; Stefanie Jegelka, MIT |
Experimental Design under Bradley Terry Model
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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
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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
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Nima Dehmamy, Northeastern University/ Neda Rohani, Northwestern University; Aggelos Katsaggelos, Northwestern University |
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks
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Pu Zhao, Northeastern University/ Sijia Liu, IBM research; AIKaidi Xu, Northeastern University; Yanzhi Wang, Northeastern University; Xue Lin, Northeastern University
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Improving Shape Deformation in Unsupervised Image-to-Image Translation
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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
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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
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Tal Wagner, MIT/ Piotr Indyk, MIT; Ilya Razenshteyn, Microsoft Research |
Hierarchical Disentangled Representations
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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
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Hao Wang, Harvard University/ Berk Ustun, Harvard University; Flavio P. Calmon, Harvard University |
One-shot Learning and Classification in Kids
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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
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Tomas Geffner, University of Massachussetts Amherst/ Justin Domke., University of Massachusetts Amherst |
Topically-Coherent Neural Language Model Conditioned on Arbitrary Features
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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
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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
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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 |