Wu, Mike

12 publications

NeurIPS 2022 Foundation Posteriors for Approximate Probabilistic Inference Mike Wu, Noah Goodman

ICLRW 2022 Know Thy Student: Interactive Learning with Gaussian Processes Rose E Wang, Mike Wu, Noah Goodman

ICLR 2021 Conditional Negative Sampling for Contrastive Learning of Visual Representations Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah Goodman

NeurIPS 2021 Improving Compositionality of Neural Networks by Decoding Representations to Inputs Mike Wu, Noah Goodman, Stefano Ermon

JAIR 2021 Optimizing for Interpretability in Deep Neural Networks with Tree Regularization Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez

ICLR 2021 Viewmaker Networks: Learning Views for Unsupervised Representation Learning Alex Tamkin, Mike Wu, Noah Goodman

AAAI 2020 Meta-Amortized Variational Inference and Learning Mike Wu, Kristy Choi, Noah D. Goodman, Stefano Ermon

AAAI 2020 Regional Tree Regularization for Interpretability in Deep Neural Networks Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

AISTATS 2019 Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference Mike Wu, Noah Goodman, Stefano Ermon

AAAI 2019 Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference Mike Wu, Milan Mosse, Noah D. Goodman, Chris Piech

AAAI 2018 Beyond Sparsity: Tree Regularization of Deep Models for Interpretability Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

NeurIPS 2018 Multimodal Generative Models for Scalable Weakly-Supervised Learning Mike Wu, Noah Goodman