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305 results on '"Mooney, Raymond J."'

52. Combining Bias and Variance Reduction Techniques for Regression Trees

Author

Suen, Yuk Lai, Melville, Prem, Mooney, Raymond J., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gama, João, editor, Camacho, Rui, editor, Brazdil, Pavel B., editor, Jorge, Alípio Mário, editor, and Torgo, Luís, editor

Published
2005
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53. Experiments on Ensembles with Missing and Noisy Data

Author

Melville, Prem, Shah, Nishit, Mihalkova, Lilyana, Mooney, Raymond J., Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Roli, Fabio, editor, and Windeatt, Terry, editor

Published
2004
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54. Learning Language from Its Perceptual Context

Author

Mooney, Raymond J., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Rocha, Ricardo, editor, and Launchbury, John, editor

Published
2011
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58. Learning to Describe Solutions for Bug Reports Based on Developer Discussions

Author

Panthaplackel, Sheena, Li, Junyi Jessy, Gligoric, Milos, and Mooney, Raymond J.

Subjects
Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, Computer Science - Computation and Language, Computation and Language (cs.CL)
Abstract

When a software bug is reported, developers engage in a discussion to collaboratively resolve it. While the solution is likely formulated within the discussion, it is often buried in a large amount of text, making it difficult to comprehend and delaying its implementation. To expedite bug resolution, we propose generating a concise natural language description of the solution by synthesizing relevant content within the discussion, which encompasses both natural language and source code. We build a corpus for this task using a novel technique for obtaining noisy supervision from repository changes linked to bug reports, with which we establish benchmarks. We also design two systems for generating a description during an ongoing discussion by classifying when sufficient context for performing the task emerges in real-time. With automated and human evaluation, we find this task to form an ideal testbed for complex reasoning in long, bimodal dialogue context., Accepted in Findings of ACL 2022

Published
2022

87. Schema Acquisition from One Example: Psychological Evidence for Explanation-Based Learning

Author

Ahn, Woo-Kyoung, Mooney, Raymond J., Brewer, William F., and DeJong, Gerald F.

Abstract

Recent explanalion-based learning (EBL) models in Al allow a computer program to learn aschema by analyzing a single example. For example. GENESIS is an EBL system which learns a planschema from a single specific instance presented in a narrative. Previous learning models in both Aland psychology have required multiple examples. This paper presents experimental evidence thatpeople can learn a plan schema from a single narrative and that the learned schema agrees with thatpredicted by EBL. This evidence suggests that GENESIS, originally constructed as a machine learningsystem, can be interpreted as a psychological model of learning a complex schema from a singleexample.

Published
1987

88. Integrated Learning of Words and their Underlying Concepts

Author

Mooney, Raymond J.

Abstract

Models of learning word meanings have generally assumed prior knowledge of the concepts to which the words refer. However, novel natural language text or discourse can often present both unknown concepts and words which refer to these concepts. Also, developmental data suggests that the learning of words and their concepts frequently occurs concurrently instead of concept learning proceeding word learning. This paper presents an integrated computational model for acquiring both word meanings and their underlying concepts concurrently. This model is implemented as a word learning component added to the CI-NLSIS explanation-based schema acquisition system for narrative understanding. A detailed example is described in which CnNHSIS learns provisional definitions for the words "kidnap", "kidnapper", and "ransom" as well as a kidnapping schema from a single narrative.

Published
1987

94. Schema acquisition from a single example

Author

Ahn, Woo-kyoung, Brewer, William F., and Mooney, Raymond J.

Subjects
Learning models (Stochastic processes) -- Research, Schemata (Cognition) -- Research, Learning, Psychology of -- Research, Psychology and mental health
Abstract

This study compares Similarity-Based Learning (SBL) and Explanation-Based Learning (EBL) approaches to schema acquisition. In SBL approaches, concept formation is based on similarity across multiple examples. However, these approaches seem to be appropriate when the learner cannot apply existing knowledge and when the concepts to be learned are nonexplanatory. EBL approaches assume that a schema can be acquired from even a single example by constructing an explanation of the example using background knowledge, and generalizing the resulting explanation. However, unlike the current EBL theories, Experiment 1 showed significant EBL occurred only when the background information learned during the experiment was actively used by the Ss. Experiment 2 showed the generality of EBL mechanisms across a variety of materials and test procedures.

Published
1992

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