Brent Heeringa

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11ranked-venue papers

2as first author

0since 2021 · last 2012

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 7 · 2 first-authorArtificial intelligence and machine learning · 3Databases, data management, data science and information retrieval · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 1 paper		Efficient and distributed learning · 100%
Databases, data mining, and information retrieval 1 paper		Data mining · 100%
Theoretical computer science 1 paper		Algorithms and data structures · 77% Mathematical optimization · 23%

Topics — the 6 heaviest of 7, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Efficient and distributed learning active learning	0.1	1	2012	Batch Active Learning via Coordinated Matching · ICML 2012
Machine learning › Efficient and distributed learning › active learning batch active learning	0.1	1	2012	Batch Active Learning via Coordinated Matching · ICML 2012
Data mining › temporal data mining time series mining	0.0	1	2002	Unsupervised Segmentation of Categorical Time Series into Episodes · ICDM 2002
Data mining › time series analysis time series segmentation	0.0	1	2002	Unsupervised Segmentation of Categorical Time Series into Episodes · ICDM 2002
Mathematical optimization discrete optimization	0.0	1	2004	Optimal Website Design with the Constrained Subtree Selection Problem · ICALP 2004
Data mining pattern mining	0.0	1	2002	Unsupervised Segmentation of Categorical Time Series into Episodes · ICDM 2002

Methods — techniques the papers use, named apart from their topics

coordinated matching · 0.1voting experts · 0.0boundary entropy · 0.0

Year	Publication	Venue	Position
2012	Batch Active Learning via Coordinated Matching Javad Azimi, Alan Fern, Xiaoli Z. Fern, Glencora Borradaile, Brent Heeringa	ICML	5
2012	Approximating Optimal Binary Decision Trees Micah Adler, Brent Heeringa	Algorithmica	2
2011	The 1-Neighbour Knapsack Problem Glencora Borradaile, Brent Heeringa, Gordon T. Wilfong	IWOCA	2
2011	Searching in Dynamic Tree-Like Partial Orders Brent Heeringa, Marius Catalin Iordan, Louis Theran	WADS	1
2010	Approximating Minimum Reset Sequences Michael Gerbush, Brent Heeringa	CIAA	2
2009	Nifty assignments abstract Assignments determine much of what students actually take away from a course. Sadly, creating successful assignments is difficult and error prone. With that in mind, the Nifty Assignments session is about promoting and sharing successful assignment ideas, and more importantly, making the assignment materials available for others to adopt. Nick Parlante, Thomas P. Murtagh, Mehran Sahami, Owen L. Astrachan, David W. Reed, Christopher A. Stone, Brent Heeringa, Karen L. Reid	SIGCSE	7
2008	Approximating Optimal Binary Decision Trees Micah Adler, Brent Heeringa	APPROX-RANDOM	2
2008	Search Space Reductions for Nearest-Neighbor Queries Micah Adler, Brent Heeringa	TAMC	2
2007	Voting experts: An unsupervised algorithm for segmenting sequences Paul R. Cohen, Niall M. Adams, Brent Heeringa	Intell. Data Anal.	3
2004	Optimal Website Design with the Constrained Subtree Selection Problem Brent Heeringa, Micah Adler	ICALP	1
2002	Unsupervised Segmentation of Categorical Time Series into Episodes abstract This paper describes an unsupervised algorithm for segmenting categorical time series into episodes. The VOTING-EXPERTS algorithm first collects statistics about the frequency and boundary entropy of ngrams, then passes a window over the series and has two "expert methods" decide where in the window boundaries should be drawn. The algorithm successfully segments text into words in four languages. The algorithm also segments time series of robot sensor data into subsequences that represent episodes in the life of the robot. We claim that VOTING-EXPERTS finds meaningful episodes in categorical time series because it exploits two statistical characteristics of meaningful episodes. Paul R. Cohen, Brent Heeringa, Niall M. Adams	ICDM	2