• Tutela: An Open-Source Tool for Assessing User-Privacy on Ethereum and Tornado Cash.
  Mike Wu, Will McTighe, Kaili Wang, Istvan A. Seres, Nick Bax, Manuel Puebla, Mariano Mendez, Federico Carrone, Tomás De Mattey, Herman O. Demaestri, Mariano Nicolini, Pedro Fontana.
  Whitepaper, 2022.
  (pdf) (code) (crypto@stanford) (golem.foundation)

• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback.
  Mike Wu, Chris Piech, Noah Goodman, Chelsea Finn.
  ArXiv, 2021.
  (pdf) (blog) (new york times) (stanford news) (HAI talk)

• Temperature as Uncertainty in Contrastive Learning.
  Oliver Zhang, Mike Wu, Jasmine Bayrooti, Noah Goodman.
  NeurIPS Workshop on Self-Supervised Learning, 2021.
  (pdf) (poster) (code)

• Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study.
  Ananya Karthik, Mike Wu, Noah Goodman, Alex Tamkin.
  NeurIPS Workshop on Self-Supervised Learning, 2021.
  (pdf) (poster)

• Improving Compositionality of Neural Networks by Decoding Representations to Inputs.
  Mike Wu, Noah Goodman, Stefano Ermon.
  NeurIPS, 2021.
  (pdf) (talk)

• HarperValleyBank: A Domain-Specific Spoken Dialog Corpus.
  Mike Wu, Jonathan Nafziger, Anthony Scodary, Andrew Maas.
  Workshop on Machine Learning in Speech and Language Processing (MLSLP), 2021.
  Used for Stanford CS224S: Spoken Language Processing.
  (pdf) (code) (poster) (cs224s)

• Optimizing for Interpretability in Deep Neural Networks with Simulable Decision Trees.
  Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez.
  Journal of Artificial Intelligence Research (JAIR) Vol. 72, 2021.
  (pdf) (journal) (CHAI talk)

• Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems.
  Ali Malik (*), Mike Wu (*), Vrinda Vasavada, Jinpeng Song, John Mitchell, Noah Goodman, Chris Piech.
  Educational Data Mining (EDM), 2021.
  (pdf)

• Viewmaker Networks: Learning Views for Unsupervised Representation Learning.
  Alex Tamkin, Mike Wu, Noah Goodman.
  ICLR, 2021.
  (pdf) (openreview) (code) (ICLR talk)

• Conditional Negative Sampling for Contrastive Learning of Visual Representations.
  Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah Goodman.
  ICLR, 2021.
  (pdf) (openreview) (code) (poster) (ICLR talk)

• A Simple Framework for Uncertainty in Contrastive Learning.
  Mike Wu, Noah Goodman.
  ArXiv, 2020.
  (pdf)

• On Mutual Information in Contrastive Learning for Visual Representations.
  Mike Wu, Chengxu Zhuang, Milan Mosse, Daniel Yamins, Noah Goodman.
  ArXiv, 2020.
  (v1 pdf) (v0 pdf) (talk)

• Variational Item Response Theory: Fast, Accurate, and Expressive.
  Mike Wu, Richard L. Davis, Benjamin W. Domingue, Chris Piech, Noah Goodman.
  Educational Data Mining (EDM), 2020.
  (pdf) (code) (video) (oral) (best paper award)

• 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.
  AAAI, 2020.
  (pdf) (oral) (poster) (AAAI talk)

• Multimodal Generative Models for Compositional Representation Learning.
  Mike Wu, Noah Goodman.
  ArXiv, 2019.
  (pdf)

• Meta-Amortized Variational Inference and Learning.
  Mike Wu (*), Kristy Choi (*), Noah Goodman, Stefano Ermon.
  AAAI, 2020.
  NeurIPS Workshop on Bayesian Deep Learning, 2019.
  (pdf) (workshop oral) (code)

• Pragmatic inference and Visual Abstraction Enable Contextual Flexibility during Visual Communication.
  Judith Fan, Robert X.D. Hawkins, Mike Wu, Noah Goodman.
  Computational Brain & Behavior (COBB) Vol. 3, 2019.
  (pdf) (code)

• Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference.
  Mike Wu, Noah Goodman, Stefano Ermon.
  AISTATS, 2019.
  (pdf) (code) (poster)

• Zero Shot Learning for CodeEducation: Rubric Sampling with Deep Learning Inference.
  Mike Wu, Milan Mosse, Noah Goodman, Chris Piech.
  AAAI, 2019.
  (pdf) (oral) (outstanding student paper award) (code) (poster)

• Multimodal Generative Models for Scalable Weakly Supervised Learning.
  Mike Wu, Noah Goodman.
  NeurIPS, 2018.
  (pdf) (code)

• Spreadsheet Probabilistic Programming.
  Mike Wu, Yura Perov, Frank Wood, Hongseok Yang, William Smith.
  PROBPROG, 2018.
  (pdf)

• Beyond Sparsity: Tree Regularization of Deep Models for Interpretability.
  Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez.
  AAAI, 2017.
  NeurIPS Workshop on Transparency in Machine Learning (TiML), 2017.
  (pdf) (code) (oral) (workshop oral) (TiML talk) (AAAI talk)

• Predicting Intervention Onset in the ICU with Switching Statespace Models.
  Marzyeh Ghassemi, Mike Wu, Michael C. Hughes, Finale Doshi-Velez.
  AMIA Clinical Research Informatics (CRI), 2017.
  (pdf) (oral) (best paper finalist)

• 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.
  Journal of the American Medical Informatics Association (JAMIA), 2016.
  (pdf) (journal)

• Edge-based Crowd Detection from Single Image Datasets.
  Mike Wu, Madhu Krishnan.
  IJCSI, 2013.
  (pdf)

• Autonomous Mapping and Navigation through Utilization of Edge-based Optical Flow and Time-to-Collision.
  Madhu Krishnan, Mike Wu, Young Kang, Sarah H. Lee.
  Journal of Engineering and Applied Sciences (ARPN), 2012.
  ISEF SemiFinalist, 2012.
  (pdf) (journal)