Search Results for author: Mike Wu

Found 28 papers, 13 papers with code

Foundation Posteriors for Approximate Probabilistic Inference

no code implementations19 May 2022 Mike Wu, Noah Goodman

Given a probabilistic program, we are interested in the task of posterior inference: estimating a latent variable given a set of observed variables.

Language Modelling Masked Language Modeling +1

Tutela: An Open-Source Tool for Assessing User-Privacy on Ethereum and Tornado Cash

1 code implementation18 Jan 2022 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

Mixers, such as Tornado Cash, were developed to preserve privacy through "mixing" transactions with those of others in an anonymity pool, making it harder to link deposits and withdrawals from the pool.

Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study

no code implementations10 Dec 2021 Ananya Karthik, Mike Wu, Noah Goodman, Alex Tamkin

Contrastive learning has made considerable progress in computer vision, outperforming supervised pretraining on a range of downstream datasets.

Contrastive Learning Image Classification

Temperature as Uncertainty in Contrastive Learning

1 code implementation8 Oct 2021 Oliver Zhang, Mike Wu, Jasmine Bayrooti, Noah Goodman

In this paper, we propose a simple way to generate uncertainty scores for many contrastive methods by re-purposing temperature, a mysterious hyperparameter used for scaling.

Contrastive Learning Out-of-Distribution Detection

Modeling Item Response Theory with Stochastic Variational Inference

no code implementations26 Aug 2021 Mike Wu, Richard L. Davis, Benjamin W. Domingue, Chris Piech, Noah Goodman

Item Response Theory (IRT) is a ubiquitous model for understanding human behaviors and attitudes based on their responses to questions.

Bayesian Inference Variational Inference

ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback

1 code implementation23 Jul 2021 Mike Wu, Noah Goodman, Chris Piech, Chelsea Finn

High-quality computer science education is limited by the difficulty of providing instructor feedback to students at scale.

Few-Shot Learning

Improving Compositionality of Neural Networks by Decoding Representations to Inputs

no code implementations NeurIPS 2021 Mike Wu, Noah Goodman, Stefano Ermon

In traditional software programs, it is easy to trace program logic from variables back to input, apply assertion statements to block erroneous behavior, and compose programs together.

Fairness Out-of-Distribution Detection

Viewmaker Networks: Learning Views for Unsupervised Representation Learning

1 code implementation ICLR 2021 Alex Tamkin, Mike Wu, Noah Goodman

However, designing these views requires considerable trial and error by human experts, hindering widespread adoption of unsupervised representation learning methods across domains and modalities.

Contrastive Learning Representation Learning

Conditional Negative Sampling for Contrastive Learning of Visual Representations

1 code implementation ICLR 2021 Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah Goodman

To do this, we introduce a family of mutual information estimators that sample negatives conditionally -- in a "ring" around each positive.

Contrastive Learning Instance Segmentation +4

Variational Item Response Theory: Fast, Accurate, and Expressive

1 code implementation1 Feb 2020 Mike Wu, Richard L. Davis, Benjamin W. Domingue, Chris Piech, Noah Goodman

Item Response Theory (IRT) is a ubiquitous model for understanding humans based on their responses to questions, used in fields as diverse as education, medicine and psychology.

Bayesian Inference

Multimodal Generative Models for Compositional Representation Learning

no code implementations11 Dec 2019 Mike Wu, Noah Goodman

As part of our derivation we find that many previous multimodal variational autoencoders used objectives that do not correctly bound the joint marginal likelihood across modalities.

Representation Learning

Gradient Boosting Machine: A Survey

no code implementations19 Aug 2019 Zhiyuan He, Danchen Lin, Thomas Lau, Mike Wu

In this survey, we discuss several different types of gradient boosting algorithms and illustrate their mathematical frameworks in detail: 1. introduction of gradient boosting leads to 2. objective function optimization, 3. loss function estimations, and 4. model constructions.

Optimizing for Interpretability in Deep Neural Networks with Tree Regularization

no code implementations14 Aug 2019 Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez

Moreover, for situations in which a single, global tree is a poor estimator, we introduce a regional tree regularizer that encourages the deep model to resemble a compact, axis-aligned decision tree in predefined, human-interpretable contexts.

Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems

no code implementations23 May 2019 Ali Malik, Mike Wu, Vrinda Vasavada, Jinpeng Song, Madison Coots, John Mitchell, Noah Goodman, Chris Piech

In this paper, we present generative grading: a novel computational approach for providing feedback at scale that is capable of accurately grading student work and providing nuanced, interpretable feedback.

Pragmatic inference and visual abstraction enable contextual flexibility during visual communication

1 code implementation11 Mar 2019 Judith Fan, Robert Hawkins, Mike Wu, Noah Goodman

On each trial, both participants were shown the same four objects, but in different locations.

Meta-Amortized Variational Inference and Learning

1 code implementation5 Feb 2019 Mike Wu, Kristy Choi, Noah Goodman, Stefano Ermon

Despite the recent success in probabilistic modeling and their applications, generative models trained using traditional inference techniques struggle to adapt to new distributions, even when the target distribution may be closely related to the ones seen during training.

Density Estimation Image Classification +1

Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference

1 code implementation5 Sep 2018 Mike Wu, Milan Mosse, Noah Goodman, Chris Piech

Rubric sampling requires minimal teacher effort, can associate feedback with specific parts of a student's solution and can articulate a student's misconceptions in the language of the instructor.

Misconceptions Zero-Shot Learning

Spreadsheet Probabilistic Programming

no code implementations14 Jun 2016 Mike Wu, Yura Perov, Frank Wood, Hongseok Yang

We demonstrate this by developing a native Excel implementation of both a particle Markov Chain Monte Carlo variant and black-box variational inference for spreadsheet probabilistic programming.

Decision Making Decision Making Under Uncertainty +2

Position and Vector Detection of Blind Spot motion with the Horn-Schunck Optical Flow

no code implementations24 Mar 2016 Stephen Yu, Mike Wu

The proposed method uses live image footage which, based on calculations of pixel motion, decides whether or not an object is in the blind-spot.

Optical Flow Estimation

Financial Market Prediction

no code implementations8 Mar 2015 Mike Wu

Given financial data from popular sites like Yahoo and the London Exchange, the presented paper attempts to model and predict stocks that can be considered "good investments".

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