36,196 Works

Multi-View Relationships for Analytics and Inference

Eric Lei
An interesting area of machine learning is methods for multi-view data, relational data whose features have been partitioned. Multi-view learning exploits relationships between views, giving it certain advantages over traditional single-view techniques, which may struggle to fi nd these
relationships or only learn them implicitly. These relationships are often especially salient in understanding the data or performing prediction. This work explores an underutilized approach in multi-view learning: to focus on multi-view relationships|the latent variables that govern
relations...

Multi-View Relationships for Analytics and Inference

Eric Lei
An interesting area of machine learning is methods for multi-view data, relational data whose features have been partitioned. Multi-view learning exploits relationships between views, giving it certain advantages over traditional single-view techniques, which may struggle to fi nd these
relationships or only learn them implicitly. These relationships are often especially salient in understanding the data or performing prediction. This work explores an underutilized approach in multi-view learning: to focus on multi-view relationships|the latent variables that govern
relations...

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees

Christoph Dann
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent can learn to make good decisions given experience and rewards in a stochastic
world. Yet popular RL algorithms that have enabled exciting successes in domains with good simulators (Go, Atari, etc) still often fail to learn in other domains because they rely on
simple heuristics for exploration. This provides additional empirical justification for essential questions around RL, specifically around algorithms that learn...

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees

Christoph Dann
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent can learn to make good decisions given experience and rewards in a stochastic
world. Yet popular RL algorithms that have enabled exciting successes in domains with good simulators (Go, Atari, etc) still often fail to learn in other domains because they rely on
simple heuristics for exploration. This provides additional empirical justification for essential questions around RL, specifically around algorithms that learn...

Estimating Probability Distributions and their Properties

Shashank Singh
This thesis studies several theoretical problems in nonparametric statistics and machine learning, mostly in the areas of nonparametric density functional estimation
(estimating an integral functional of the population distribution from which the data are drawn) and nonparametric density estimation (estimating the entire population distribution from which the data are drawn). A consistent theme is that, although nonparametric density estimation is traditionally thought to be intractable in highdimensions, several equally (or more) useful tasks are relatively more...

Estimating Probability Distributions and their Properties

Shashank Singh
This thesis studies several theoretical problems in nonparametric statistics and machine learning, mostly in the areas of nonparametric density functional estimation
(estimating an integral functional of the population distribution from which the data are drawn) and nonparametric density estimation (estimating the entire population distribution from which the data are drawn). A consistent theme is that, although nonparametric density estimation is traditionally thought to be intractable in highdimensions, several equally (or more) useful tasks are relatively more...

Learning Generative Models using Transformations

Chun-Liang Li
One of the fundamental problems in machine learning and statistics is learning generative models of data. Explicit generative models, which model probability densities of data, have been intensively studied in numerous applications.
However, it is usually difficult to model complex data, such as natural images, by using combinations of simple parametric distributions. Implicit generative models (IGMs), which model transformations between known source distributions and target distributions to simulate the sampling process without
specifying densities explicitly, regain its...

Learning Generative Models using Transformations

Chun-Liang Li
One of the fundamental problems in machine learning and statistics is learning generative models of data. Explicit generative models, which model probability densities of data, have been intensively studied in numerous applications.
However, it is usually difficult to model complex data, such as natural images, by using combinations of simple parametric distributions. Implicit generative models (IGMs), which model transformations between known source distributions and target distributions to simulate the sampling process without
specifying densities explicitly, regain its...

Supplementary Material for Spherical Indexing of Complex Microstructures paper

Marc Degraef, William Lenthe, Lionel Germain, Marie-Rita Chini & Nathalie Gey
Two experimental EBSD data sets acquired at the Universite de Lorraine, France, along with data analysis using the commercial indexing software as well as a new spherical indexing approach. All input and output files from the data analysis are made available.

Supplementary Material for Spherical Indexing of Complex Microstructures paper

Marc Degraef, William Lenthe, Lionel Germain, Marie-Rita Chini & Nathalie Gey
Two experimental EBSD data sets acquired at the Universite de Lorraine, France, along with data analysis using the commercial indexing software as well as a new spherical indexing approach. All input and output files from the data analysis are made available.

Data from Think-aloud interviews: A tool for exploring student statistical reasoning

Alex Reinhart, Ciaran Evans, Amanda Sue Luby, Josue Orellana Arreag, Mikaela Meyer, Jerzy Wieczorek, Peter Elliott, Philipp Burckhardt & Rebecca Nugent
This submission includes all assessment data from the paper "Think-aloud interviews: A tool for exploring student statistical reasoning", as well as code necessary to reproduce the figures and tables presented in the paper, and the supplemental materials for the paper. See the file README.txt for full description of all files. Each student interviewed has been given a fictitious name.

Data from Think-aloud interviews: A tool for exploring student statistical reasoning

Alex Reinhart, Ciaran Evans, Amanda Sue Luby, Josue Orellana Arreag, Mikaela Meyer, Jerzy Wieczorek, Peter Elliott, Philipp Burckhardt & Rebecca Nugent
This submission includes all assessment data from the paper "Think-aloud interviews: A tool for exploring student statistical reasoning", as well as code necessary to reproduce the figures and tables presented in the paper, and the supplemental materials for the paper. See the file README.txt for full description of all files. Each student interviewed has been given a fictitious name.

Data from Think-aloud interviews: A tool for exploring student statistical reasoning

Alex Reinhart, Ciaran Evans, Amanda Sue Luby, Josue Orellana Arreag, Mikaela Meyer, Jerzy Wieczorek, Peter Elliott, Philipp Burckhardt & Rebecca Nugent
This submission includes all assessment data from the paper "Think-aloud interviews: A tool for exploring student statistical reasoning", as well as code necessary to reproduce the figures and tables presented in the paper, and the supplemental materials for the paper. See the file README.txt for full description of all files. Each student interviewed has been given a fictitious name.

Safe Data Gathering in Physical Spaces

Sankalp Arora
Reliable and efficient acquisition of data from physical spaces has widespread applications in industry, policy, defense, and humanitarian work. Unmanned Aerial Vehicles (UAVs) are an excellent choice for data gathering applications, due to their capability of gaining information at multiple scales. A robust data gathering system needs to reason about multi-resolution nature of information gathering while being safe, and cognizant of its sensory and battery limitations. The state of the art algorithms with provable worst-case...

Occupational Mobility and Carotid Artery Intima-Media Thickness: Findings From the Coronary Artery Risk Development in Young Adults Study

Denise Janicki-Deverts, Sheldon Cohen, Karen A. Matthews, David R Jacobs & Nancy Adler
OBJECTIVE: To examine whether a 10-year change in occupational standing is related to carotid artery intima-media thickness (IMT) 5 years later. METHODS: Data were obtained from 2350 participants in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Occupational standing was measured at the Year 5 and 15 CARDIA follow-up examinations when participants were 30.2 (standard deviation = 3.6) and 40.2 (standard deviation = 3.6) years of age, respectively. IMT (common carotid artery [CCA],...

Formal Verification of Infinite State Systems Using Boolean Methods

Randal E. Bryant
Most successful automated formal verification tools are based on a bit-level model of computation, where a set of Boolean state variables encodes the system state. Using powerful inference engines, such as Binary Decision Diagrams (BDDs) and Boolean satisfiability (SAT) checkers, symbolic model checkers and similar tools can analyze all possible behaviors of very large, finite-state systems.

Health benefits exceed by 70 % costs to control stationary source air pollution

Lester B Lave & Eugene P. Seskin
The 1960's was a decade of U.S. idealism-there was a belief that our society could accomplish anything it set its mind to do. The decade opened with President Kennedy declaring that we would put a man on the moon by 1970. Social injustices were to be righted by a series of civil rights acts that pushed aside economic as well as legal barriers. Finally, President Johnson believed that we could have Great Society programs at...

Higher-dimensional Voronoi diagrams in linear expected time

Rex A. Dwyer
Computer Science Department

A Bio-sensing and Reinforcement Learning Control System for Personalized Thermal Comfort and Energy Efficiency

Chenlu Zhang
A comfortable indoor thermal environment plays a crucial role in preserving occupant health and productivity. In most office building today, the indoor thermal environment is regulated by heating, cooling, and air-conditioning
(HVAC) systems with static schedule-based rules. While prevalent, this control strategy has resulted in low thermal satisfaction rates and energy waste. A growing number of researchers are focusing on occupant-centric building
controls and applying various advanced control methods to improve thermal comfort and energy efficiency. However,...

Micromachining Metrology: Measurement and Analysis of Dynamic Tool-tip Trajectory when using Ultra-High-Speed Spindles

Sudhanshu Nahata
There is a growing demand for miniature, high-precision components and devices with micro-scale features for applications in biomedical systems, aerospace structures, and energy storage/conversion systems. Mechanical micromachining has become a leading approach to address this demand. In micromachining, a micro-scale cutting tool, such as a micro-endmill with a diameter as small as 10 um, is rotated by an ultra-high-speed (UHS) spindle (speeds greater than 60,000 rpm, reaching up to 500,000 rpm) to mechanically remove the...

The characterization problem for Hoare logics

E. M. Clarke
Computer Science Department

Upgrading design systems

Sarosh Talukdar & Carnegie Mellon University.Engineering Design Research Center.
Technical Report

A theory of reading: From eye fixations to comprehension

Marcel Just & Patricia A. Carpenter
Department of Psychology

Teaching Programming to Musicians

Frances K Dannenberg, Roger B Dannenberg & Philip L Miller
A new approach has been developed for teaching programming to musicians. The approach uses personal computers with music synthesis capabilities, and students write programs in order to realize musical compositions. Our curriculum emphasizes abstraction in programming by the early introduction of high-level concepts and the late introduction of programming language details. We also emphasize abstraction by relating programming concepts to musical concepts which are already familiar to our students. We have successfully used this curriculum...

Autonomy Infused Teleoperation with Application to BCI Manipulation

Katharina Muelling, Arun Venkatraman, Jean-Sebastien Valois, John E. Downey, Jeffrey M. Weiss, Shervin Javdani, Martial Hebert, Andrew B. Schwartz, Jennifer L. Collinger & J. Andrew Bagnell
Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain- Computer Interfaces (BCIs) exacerbates these problems through especially noisy and erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of computer vision, user intent inference, and arbitration between the human input and autonomous control schemes. Adjustable levels of...

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