Learning without Annotation

Supervised learning has worked very well in practice but yet they require exhaustive labelled data, posing a barrier to widespread adoption. My research works towards a holy grail of unsupervised learning that can either learn without annotation completely or with noisy and easy to obtain labels. I like to think about "representation learning" algorithms that learn to capture the inherent structure of the data itself (e.g. the distribution or invariances in properties), or multimodality, where an assortment of images, text, videos, audio clips, etc each provides a stronger learning signal we could use to build a richer and more abstract representation.


Preprints

Submitted to ICLR 2020
Viewmaker Networks: Learning Views for Unsupervised Representation Learning

Alex Tamkin, Mike Wu, Noah Goodman

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Submitted to ICLR 2020
A Simple Framework for Uncertainty in Contrastive Learning

Mike Wu, Noah Goodman

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Submitted to ICLR 2020
Conditional Negative Sampling for Contrastive Learning of Visual Representations

Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah Goodman

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Submitted to JMLR
Multimodal Generative Models for Compositional Representation Learning

Mike Wu, Noah Goodman

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Submitted to JAIR
Optimizing for Interpretability in Deep Neural Networks with Simulable Decision Trees

Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez

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Publications

ArXiv
On Mutual Information in Contrastive Learning for Visual Representations

Mike Wu, Chengxu Zhuang, Milan Mosse, Daniel Yamins, Noah Goodman

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EDM 2020
Variational Item Response Theory: Fast, Accurate, and Expressive

Mike Wu, Richard L. Davis, Benjamin W. Domingue, Chris Piech, Noah Goodman

PDF Oral Best Paper
ArXiv
Generative Grading: Neural Approximate Parsing for Automated Student Feedback

Ali Malik (*), Mike Wu (*), Vrinda Vasavada, Jinpeng Song, John Mitchell, Noah Goodman, Chris Piech

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AAAI 2020
Regional Tree Regularization for Interpretability in Black Box Models

Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

PDF Oral
AAAI 2020, NeurIPS BDL 2019
Meta-Amortized Variational Inference and Learning

Mike Wu (*), Kristy Choi (*), Noah Goodman, Stefano Ermon

PDF BDL Oral
COBB 2019
Pragmatic inference and Visual Abstraction Enable Contextual Flexibility during Visual Communication

Judith Fan, Robert X.D. Hawkins, Mike Wu, Noah Goodman

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AISTATS 2019
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference

Mike Wu, Noah Goodman, Stefano Ermon

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AAAI 2019
Zero Shot Learning for CodeEducation: Rubric Sampling with Deep Learning Inference

Mike Wu, Milan Mosse, Noah Goodman, Chris Piech

PDF Oral Best Student Paper
NeurIPS 2018
Multimodal Generative Models for Scalable Weakly Supervised Learning

Mike Wu, Noah Goodman

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PROBPROG 2018
Spreadsheet Probabilistic Programming

Mike Wu, Yura Perov, Frank Wood, Hongseok Yang, William Smith

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AAAI 2017, NeurIPS TiML 2017
Tree Regularization of Deep Models for Interpretability

Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

PDF AAAI Oral TiML Oral
AMIA CRI 2017
Predicting Intervention Onset in the ICU with Switching Statespace Models

Marzyeh Ghassemi, Mike Wu, Michael C. Hughes, Finale Doshi-Velez

PDF Oral Best Paper Finalist
JAMIA 2016
Understanding Vassopressor Intervention and Weaning: Risk Prediction in a Public Heterogeneous Clinical Time Series Database

Mike Wu, Marzyeh Ghassemi, Mengling Feng, Leo Anthony Celi, Peter Szolovitz, Finale Doshi-Velez

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IJCSI 2013
Edge-based Crowd Detection from Single Image Datasets

Mike Wu, Madhu Krishnan

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ARPN 2012, ISEF Semifinalist 2012
Autonomous Mapping and Navigation through Utilization of Edge-based Optical Flow and Time-to-Collision

Madhu Krishnan, Mike Wu, Young Kang, Sarah H. Lee

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Conference Abstracts

VSS 2018
Modeling Contextual Flexibility in Visual Communication

Judith Fan, Robert X.D. Hawkins, Mike Wu, Noah Goodman

AAS 2018, Undergrad Thesis
Investigating the Cosmic Web with Topological Data Analysis

Jessi Cisewski-Kehe, Mike Wu, Brittany Fasy, Wojciech Hellwing, Mark Lovell, Alessandro Rinaldo, Larry Wasserman

XSEDE 2011, Siemens Semifinalist 2012, ISEF Finalist 2011: 3rd place
Position and Vector Detection of Blind Spot motion with the Horn-Schunck Optical Flow

Stephen Yu, Mike Wu

XSEDE Best Student Poster