Author name cluster

Kayur Patel

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

AAAI Conference 2015 Conference Paper

Scalable and Interpretable Data Representation for High-Dimensional, Complex Data

Been Kim
Kayur Patel
Afshin Rostamizadeh
Julie Shah

The majority of machine learning research has been focused on building models and inference techniques with sound mathematical properties and cutting edge performance. Little attention has been devoted to the development of data representation that can be used to improve a user’s ability to interpret the data and machine learning models to solve real-world problems. In this paper, we quantitatively and qualitatively evaluate an efficient, accurate and scalable feature-compression method using latent Dirichlet allocation for discrete data. This representation can effectively communicate the characteristics of high-dimensional, complex data points. We show that the improvement of a user’s interpretability through the use of a topic modeling-based compression technique is statistically significant, according to a number of metrics, when compared with other representations. Also, we find that this representation is scalable — it maintains alignment with human classification accuracy as an increasing number of data points are shown. In addition, the learned topic layer can semantically deliver meaningful information to users that could potentially aid human reasoning about data characteristics in connection with compressed topic space.

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IJCAI Conference 2011 Conference Paper

Using Multiple Models to Understand Data

Kayur Patel
Steven M. Drucker
James Fogarty
Ashish Kapoor
Desney S. Tan

A human's ability to diagnose errors, gather data, and generate features in order to build better models is largely untapped. We hypothesize that analyzing results from multiple models can help people diagnose errors by understanding relationships among data, features, and algorithms. These relationships might otherwise be masked by the bias inherent to any individual model. We demonstrate this approach in our Prospect system, show how multiple models can be used to detect label noise and aid in generating new features, and validate our methods in a pair of experiments.

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AAAI Conference 2008 Conference Paper

Examining Difficulties Software Developers Encounter in the Adoption of Statistical Machine Learning

Kayur Patel
James A. Landay

Statistical machine learning continues to show promise as a tool for addressing complex problems in a variety of domains. An increasing number of developers are therefore looking to use statistical machine learning algorithms within applications. We have conducted two initial studies examining the difficulties that developers encounter when creating a statistical machine learning component of a larger application. We first interviewed researchers with experience integrating statistical machine learning into applications. We then sought to directly observe and quantify some of the behavior described in our interviews using a laboratory study of developers attempting to build a simple application that uses statistical machine learning. This paper presents the difficulties we observed in our studies, discusses current challenges to developer adoption of statistical machine learning, and proposes potential approaches to better supporting developers creating statistical machine learning components of applications.

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AAAI Conference 2008 Conference Paper

Intelligence in Wikipedia

Daniel S. Weld
Eytan Adar
James Fogarty
Kayur Patel

The Intelligence in Wikipedia project at the University of Washington is combining self-supervised information extraction (IE) techniques with a mixed initiative interface designed to encourage communal content creation (CCC). Since IE and CCC are each powerful ways to produce large amounts of structured information, they have been studied extensively — but only in isolation. By combining the two methods in a virtuous feedback cycle, we aim for substantial synergy. While previous papers have described the details of individual aspects of our endeavor [25, 26, 24, 13], this report provides an overview of the project’s progress and vision.

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ICRA Conference 2005 Conference Paper

Active Sensing for High-Speed Offroad Driving

Kayur Patel
Walter Macklem
Sebastian Thrun
Michael Montemerlo

In this paper we propose an active control strategy for scanning laser sensors on autonomous vehicles traveling offroad at high speeds. As speed increases the amount of sensor information about the terrain decreases. We address the problem of sensor control in the context of this speed-coverage trade off. The algorithm and testing methodologies are described with results comparing our active sensing method to a passive sensing method.

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