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Tom M. Mitchell

5 accepted papers

2020

Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction

NeurIPS 2020poster

How meaning is represented in the brain is still one of the big open questions in neuroscience. Does a word (e.g., bird) always have the same representation, or does the task under which the word is processed alter its representation (answering

2019

Game Design for Eliciting Distinguishable Behavior

NeurIPS 2019poster

The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems. Approaches to infer such traits range from surveys to manually-constructed experiments and games. However, these traditional games are limited becaus…

Cited by 2SourcePDFScholar
2019

Learning Data Manipulation for Augmentation and Weighting

NeurIPS 2019poster

Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training. Previous work has studied various rule- or learning-based approaches designed for specific types of data manipulation. In this work, we propose a new method that…

2018

Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems

NeurIPS 2018poster

As machine learning becomes more widely used in practice, we need new methods to build complex intelligent systems that integrate learning with existing software, and with domain knowledge encoded as rules. As a case study, we present such a system that learns to parse Newtonian physics problems in…

Cited by 38SourcePDFScholar
2017

Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach

NeurIPS 2017poster

We propose an efficient method to estimate the accuracy of classifiers using only unlabeled data. We consider a setting with multiple classification problems where the target classes may be tied together through logical constraints. For example, a set of classes may be mutually exclusive, meaning th…

Cited by 75SourcePDFScholar