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Mike Wu
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2020 – today
- 2022
- [b1]Mike Wu:
Extensions and applications of deep probabilistic inference for generative models. Stanford University, USA, 2022 - [c27]Mike Wu, Noah D. Goodman:
Foundation Posteriors for Approximate Probabilistic Inference. NeurIPS 2022 - [i29]Mike Wu, Will McTighe, Kaili Wang, István András Seres, Nick Bax, Manuel Puebla, Mariano Mendez, Federico Carrone, Tomás De Mattey, Herman O. Demaestri, Mariano Nicolini, Pedro Fontana:
Tutela: An Open-Source Tool for Assessing User-Privacy on Ethereum and Tornado Cash. CoRR abs/2201.06811 (2022) - [i28]Rose E. Wang, Mike Wu, Noah D. Goodman:
Know Thy Student: Interactive Learning with Gaussian Processes. CoRR abs/2204.12072 (2022) - [i27]Mike Wu, Will McTighe:
Constant Power Root Market Makers. CoRR abs/2205.07452 (2022) - [i26]Mike Wu, Noah D. Goodman:
Foundation Posteriors for Approximate Probabilistic Inference. CoRR abs/2205.09735 (2022) - 2021
- [j4]Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez:
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. J. Artif. Intell. Res. 72: 1-37 (2021) - [c26]Ali Malik, Mike Wu, Vrinda Vasavada, Jinpeng Song, Madison Coots, John Mitchell, Noah D. Goodman, Chris Piech:
Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems. EDM 2021 - [c25]Alex Tamkin, Mike Wu, Noah D. Goodman:
Viewmaker Networks: Learning Views for Unsupervised Representation Learning. ICLR 2021 - [c24]Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah D. Goodman:
Conditional Negative Sampling for Contrastive Learning of Visual Representations. ICLR 2021 - [c23]Mike Wu, Noah D. Goodman, Stefano Ermon:
Improving Compositionality of Neural Networks by Decoding Representations to Inputs. NeurIPS 2021: 26689-26700 - [i25]Mike Wu, Noah D. Goodman, Stefano Ermon:
Improving Compositionality of Neural Networks by Decoding Representations to Inputs. CoRR abs/2106.00769 (2021) - [i24]Mike Wu, Noah D. Goodman, Chris Piech, Chelsea Finn:
ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback. CoRR abs/2107.14035 (2021) - [i23]Mike Wu, Richard Lee Davis, Benjamin W. Domingue, Chris Piech, Noah D. Goodman:
Modeling Item Response Theory with Stochastic Variational Inference. CoRR abs/2108.11579 (2021) - [i22]Oliver Zhang, Mike Wu, Jasmine Bayrooti, Noah D. Goodman:
Temperature as Uncertainty in Contrastive Learning. CoRR abs/2110.04403 (2021) - [i21]Ananya Karthik, Mike Wu, Noah D. Goodman, Alex Tamkin:
Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study. CoRR abs/2112.05340 (2021) - 2020
- [c22]Mike Wu, Kristy Choi, Noah D. Goodman, Stefano Ermon:
Meta-Amortized Variational Inference and Learning. AAAI 2020: 6404-6412 - [c21]Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth
, Finale Doshi-Velez:
Regional Tree Regularization for Interpretability in Deep Neural Networks. AAAI 2020: 6413-6421 - [c20]Mike Wu, Richard Lee Davis, Benjamin W. Domingue, Chris Piech, Noah D. Goodman:
Variational Item Response Theory: Fast, Accurate, and Expressive. EDM 2020 - [i20]Mike Wu, Richard Lee Davis, Benjamin W. Domingue, Chris Piech, Noah D. Goodman:
Variational Item Response Theory: Fast, Accurate, and Expressive. CoRR abs/2002.00276 (2020) - [i19]Mike Wu, Chengxu Zhuang, Milan Mosse, Daniel Yamins, Noah D. Goodman:
On Mutual Information in Contrastive Learning for Visual Representations. CoRR abs/2005.13149 (2020) - [i18]Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah D. Goodman:
Conditional Negative Sampling for Contrastive Learning of Visual Representations. CoRR abs/2010.02037 (2020) - [i17]Mike Wu, Noah D. Goodman:
A Simple Framework for Uncertainty in Contrastive Learning. CoRR abs/2010.02038 (2020) - [i16]Alex Tamkin, Mike Wu, Noah D. Goodman:
Viewmaker Networks: Learning Views for Unsupervised Representation Learning. CoRR abs/2010.07432 (2020) - [i15]Mike Wu, Jonathan Nafziger, Anthony Scodary, Andrew Maas:
HarperValleyBank: A Domain-Specific Spoken Dialog Corpus. CoRR abs/2010.13929 (2020)
2010 – 2019
- 2019
- [c19]Mike Wu, Milan Mosse, Noah D. Goodman, Chris Piech:
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference. AAAI 2019: 782-790 - [c18]Mike Wu, Noah D. Goodman, Stefano Ermon:
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference. AISTATS 2019: 2877-2886 - [i14]Kristy Choi, Mike Wu, Noah D. Goodman, Stefano Ermon:
Meta-Amortized Variational Inference and Learning. CoRR abs/1902.01950 (2019) - [i13]Judith W. Fan, Robert X. D. Hawkins, Mike Wu, Noah D. Goodman:
Pragmatic inference and visual abstraction enable contextual flexibility during visual communication. CoRR abs/1903.04448 (2019) - [i12]Ali Malik, Mike Wu, Vrinda Vasavada, Jinpeng Song, John Mitchell, Noah D. Goodman, Chris Piech:
Generative Grading: Neural Approximate Parsing for Automated Student Feedback. CoRR abs/1905.09916 (2019) - [i11]Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez:
Regional Tree Regularization for Interpretability in Black Box Models. CoRR abs/1908.04494 (2019) - [i10]Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez:
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. CoRR abs/1908.05254 (2019) - [i9]Zhiyuan He, Danchen Lin, Thomas Lau, Mike Wu:
Gradient Boosting Machine: A Survey. CoRR abs/1908.06951 (2019) - [i8]Mike Wu, Noah D. Goodman:
Multimodal Generative Models for Compositional Representation Learning. CoRR abs/1912.05075 (2019) - 2018
- [c17]Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. AAAI 2018: 1670-1678 - [c16]Mike Wu, Noah D. Goodman:
Multimodal Generative Models for Scalable Weakly-Supervised Learning. NeurIPS 2018: 5580-5590 - [i7]Mike Wu, Noah D. Goodman:
Multimodal Generative Models for Scalable Weakly-Supervised Learning. CoRR abs/1802.05335 (2018) - [i6]Mike Wu, Milan Mosse, Noah D. Goodman, Chris Piech:
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference. CoRR abs/1809.01357 (2018) - [i5]Mike Wu, Noah D. Goodman, Stefano Ermon:
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference. CoRR abs/1810.02555 (2018) - 2017
- [j3]Mike Wu, Marzyeh Ghassemi, Mengling Feng, Leo A. Celi, Peter Szolovits, Finale Doshi-Velez:
Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database. J. Am. Medical Informatics Assoc. 24(3): 488-495 (2017) - [c15]Marzyeh Ghassemi, Mike Wu, Michael C. Hughes, Peter Szolovits, Finale Doshi-Velez:
Predicting intervention onset in the ICU with switching state space models. CRI 2017 - [i4]Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez:
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. CoRR abs/1711.06178 (2017) - 2016
- [i3]Stephen Yu, Mike Wu:
Position and Vector Detection of Blind Spot motion with the Horn-Schunck Optical Flow. CoRR abs/1603.07625 (2016) - [i2]Mike Wu, Yura N. Perov, Frank D. Wood, Hongseok Yang:
Spreadsheet Probabilistic Programming. CoRR abs/1606.04216 (2016) - 2015
- [i1]Mike Wu:
Financial Market Prediction. CoRR abs/1503.02328 (2015) - 2012
- [c14]Hanna Suominen, Karl Kreiner, Mike Wu, Leif Hanlen:
Towards Ease of Building Legos in Assessing eHealth Language Technologies A RESTful Laboratory for Data and Software. CLEF (Online Working Notes/Labs/Workshop) 2012 - 2010
- [c13]Mike Wu, Ronald M. Baecker, Brian Richards:
Field evaluation of a collaborative memory aid for persons with amnesia and their family members. ASSETS 2010: 51-58
2000 – 2009
- 2009
- [c12]Mike Wu, Abhishek Ranjan, Khai N. Truong
An exploration of social requirements for exercise group formation. CHI 2009: 79-82 - 2008
- [j2]Mike Wu:
Lifelong Interactions - Memory impairment is a family affair. Interactions 15(5): 21-23 (2008) - [c11]Mike Wu, Jeremy P. Birnholtz, Brian Richards, Ronald Baecker, Michael Massimi:
Collaborating to remember: a distributed cognition account of families coping with memory impairments. CHI 2008: 825-834 - 2006
- [j1]Chia Shen, Kathy Ryall, Clifton Forlines, Alan Esenther, Frédéric Vernier, Katherine Everitt, Mike Wu, Daniel Wigdor, Meredith Ringel Morris, Mark S. Hancock
Informing the Design of Direct-Touch Tabletops. IEEE Computer Graphics and Applications 26(5): 36-46 (2006) - [c10]Julien Epps
A study of hand shape use in tabletop gesture interaction. CHI Extended Abstracts 2006: 748-753 - [c9]Fang Chen
ViCAT: Visualisation and Interaction on a Collaborative Access Table. Tabletop 2006: 59-62 - [c8]Mike Wu, Chia Shen, Kathy Ryall, Clifton Forlines, Ravin Balakrishnan:
Gesture Registration, Relaxation, and Reuse for Multi-Point Direct-Touch Surfaces. Tabletop 2006: 185-192 - 2005
- [c7]Mike Wu, Ronald Baecker, Brian Richards:
Participatory design of an orientation aid for amnesics. CHI 2005: 511-520 - [c6]Fang Chen, Eric H. C. Choi, Julien Epps, Serge Lichman, Natalie Ruiz, Yu (David) Shi, Ronnie Taib, Mike Wu:
A study of manual gesture-based selection for the PEMMI multimodal transport management interface. ICMI 2005: 274-281 - [c5]Clifton Forlines, Chia Shen, Frédéric Vernier, Mike Wu:
Under My Finger: Human Factors in Pushing and Rotating Documents Across the Table. INTERACT 2005: 994-997 - 2004
- [c4]Mike Wu, Brian Richards, Ronald Baecker:
Participatory design with individuals who have amnesia. PDC 2004: 214-223 - 2003
- [c3]James Dai, Mike Wu, Jonathan Cohen, Maria M. Klawe:
Primeclimb: Designing to Facilitate Mediated Collaborative Inquiry. CSCL 2003: 31-35 - [c2]Mark Thomson, Simon Boland, Mike Wu, Julien Epps, Michael Smithers:
Decomposition of speech into voiced and unvoiced components based on a state-space signal model. ICASSP (1) 2003: 160-163 - [c1]Mike Wu, Ravin Balakrishnan:
Multi-finger and whole hand gestural interaction techniques for multi-user tabletop displays. UIST 2003: 193-202
Coauthor Index
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