Algorithms Seminar/Fall10

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| Sep 8 || Epsilon-Quantizations and Epsilon-SIPs || [http://www.cs.utah.edu/~jeffp/papers/uncertaintyESA09.pdf Shape Fitting on Point Sets with Probability Distributions] ||
| Sep 8 || Epsilon-Quantizations and Epsilon-SIPs || [http://www.cs.utah.edu/~jeffp/papers/uncertaintyESA09.pdf Shape Fitting on Point Sets with Probability Distributions] ||
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| Sep 15 || Geometry on Imprecise Points || [http://www.ics.uci.edu/~mloffler/publications/Basic_Geometric_Measures_on_Imprecise_Points.html Basic Geometry Measures on Imprecise Points] ||  
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| Sep 15 || Geometry on Imprecise Points || [http://www.ics.uci.edu/~mloffler/publications/Basic_Geometric_Measures_on_Imprecise_Points.html Basic Geometry Measures on Imprecise Points] and maybe [http://www.ics.uci.edu/~mloffler/publications/Largest_and_Smallest_Convex_Hulls_for_Imprecise_Points.html Largest and Smallest Convex Hulls on Imprecise Points] ||  
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| Sep 22 || Imprecise Convex Hulls || [http://www.ics.uci.edu/~mloffler/publications/Largest_and_Smallest_Convex_Hulls_for_Imprecise_Points.html Largest and Smallest Convex Hulls on Imprecise Points] ||
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| Sep 22 || Hardness of Uncertainty Problems || [http://www.cs.washington.edu/homes/suciu/vldbj-probdb.pdf Efficient Query Evaluation on Probabilistic Databases][see Jeff for preprint] ||  
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| Sep 29 || Hardness of Uncertainty Problems || [http://www.cs.washington.edu/homes/suciu/vldbj-probdb.pdf Efficient Query Evaluation on Probabilistic Databases][see Jeff for preprint] ||  
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| colspan="4" bgcolor="#dddddd" style = "text-align:center" | '''Databases'''  
| colspan="4" bgcolor="#dddddd" style = "text-align:center" | '''Databases'''  
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| Sep 29 || Sketching and Streaming || [http://dimacs.rutgers.edu/~graham/pubs/papers/probstreams.pdf Sketching probabilistic data streams] ||
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| Oct 6 || Histograms || [http://dimacs.rutgers.edu/~graham/pubs/html/CormodeGarofalakis09.html Histograms and wavelets on probabilistic data]  [http://www.cs.umass.edu/~mcgregor/papers/09-vldb.pdf Probabilistic Histograms for Probabilistic Data] ||  
| Oct 6 || Histograms || [http://dimacs.rutgers.edu/~graham/pubs/html/CormodeGarofalakis09.html Histograms and wavelets on probabilistic data]  [http://www.cs.umass.edu/~mcgregor/papers/09-vldb.pdf Probabilistic Histograms for Probabilistic Data] ||  
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| Oct 27 || Clustering ||[http://dimacs.rutgers.edu/~graham/pubs/papers/pclust.pdf Approximation Algorithms for Clustering Uncertain Data] ||  
| Oct 27 || Clustering ||[http://dimacs.rutgers.edu/~graham/pubs/papers/pclust.pdf Approximation Algorithms for Clustering Uncertain Data] ||  
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| Nov 3 || Sketching and Streaming || [http://dimacs.rutgers.edu/~graham/pubs/papers/probstreams.pdf Sketching probabilistic data streams] ||  
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| colspan="4" bgcolor="#dddddd" style = "text-align:center" | '''Visualization'''
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| Nov 3 || Surfaces || ||
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| Nov 10 || Indexing /  Range Searching || [http://www.cse.ust.hk/~yike/pods09-urange.pdf Indexing Uncertain Data] with [http://www.cse.ust.hk/~yike/urange-shorttalk.pdf slides]||  
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| Nov 10 || Volumes and Transfer Functions || ||
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| colspan="4" bgcolor="#dddddd" style = "text-align:center" | '''Machine Learning And Statistics'''  
| colspan="4" bgcolor="#dddddd" style = "text-align:center" | '''Machine Learning And Statistics'''  

Revision as of 04:46, 21 May 2010

Modelling Data With Uncertainty

Wed 1:25-2:45pm

MEB 3147 (LCR)

Contents

Synopsis

We will cover many recent developments in the modeling and processing of uncertain data in computer science. First we will overview different models of uncertainty, tracing their origins and motivation (Many-world models in databases, imprecision models in geometry, probabilistic models in databases). Then we will proceed to survey recent developments in computational geometry, databases, and machine learning and statistics. Highlights in computational geometry will describe algorithmic analysis to answer basic geometry problems, either with respect to worst case bounds or by formally approximating the distributions. Work in databases has focused on data structures to quickly retrieve summary queries of large data sets with respect to the underlying data uncertainty. This topic has been studied for longer in machine learning and statistics so we will summarize classic techniques and see some recent developments. Through this seminar participants will gain an understanding of the state-of-the-art in modeling and processing uncertain data and will be exposed to several important open problems and exciting research directions.

Schedule

(subject to change)

Date Topic Paper(s) Presenter
Aug 25 Models of Data Uncertainty Jeff Phillips
Geometry
Sep 1 Range Spaces and epsilon-Samples [??? others]Sariel's Notes
Sep 8 Epsilon-Quantizations and Epsilon-SIPs Shape Fitting on Point Sets with Probability Distributions
Sep 15 Geometry on Imprecise Points Basic Geometry Measures on Imprecise Points and maybe Largest and Smallest Convex Hulls on Imprecise Points
Sep 22 Hardness of Uncertainty Problems Efficient Query Evaluation on Probabilistic Databases[see Jeff for preprint]
Databases
Sep 29 Sketching and Streaming Sketching probabilistic data streams
Oct 6 Histograms Histograms and wavelets on probabilistic data Probabilistic Histograms for Probabilistic Data
Oct 20 Ranking A Unified Approach to Ranking in Probabilistic Databases with slides and Semantics of ranking queries for probabilistic data and expected ranks
Oct 27 Clustering Approximation Algorithms for Clustering Uncertain Data
Visualization
Nov 3 Surfaces
Nov 10 Volumes and Transfer Functions
Machine Learning And Statistics
Nov 17 Particle Filters
Nov 24 Support Vector Machines Support Vector Classification with Input Data Uncertainty
Dec 1 Markov Random Fields
Dec 8 Multi-Armed Bandits

Participants

Readings

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