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CSE 590AI - Autumn 2003
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Statistical Relational AI
Greetings, and welcome to 590AI, Autumn 2003. The seminar this quarter
is held jointly with the CSE/Statistics seminar on data mining and
statistical learning, and is on the topic of statistical relational AI.
This a hot area whose goal is to apply probabilistic reasoning and
statistical learning to powerful representations like relational
databases, first-order logic, and object-oriented languages.
Its applications include link-based Web search, information
extraction, recommender systems, social network modeling, viral
marketing, natural language processing, bioinformatics, information
integration, counter-terrorism, ubiquitous computing, and many
others. UW is an active center of research in this area; check out
this
paper for an overview.
If you're interested in AI, then be sure to sign up for this
seminar!
Sign Up to Present a Paper
Every week we will present and discuss a paper from this summer's
conferences. See the schedule so far and a list of suggested papers
below. Sign up to present a paper by emailing Pedro
(pedrod@cs.washington.edu).
You can present a paper from the suggested list or propose another one.
Send questions, comments and suggestions to Pedro.
Mailing List
Please sign up. To subscribe, visit the
mailing list home page. Alternatively, you can use the email
interface to subscribe; send email to cse590ai-request@cs with the word
"help" in the subject to receive a list of email command options.
Read the archives
here.
Meeting Time and Place
Wednesdays 4:30-5:20 in
EE1 045.
Schedule
| Day |
Paper/Topic |
Presenter |
| Oct 1 |
Introduction and motivation (see also
this paper) |
Pedro Domingos |
| Oct 8 |
Review of background |
Matt Richardson |
| Oct 15 |
Kubica, Moore, Cohn & Schneider, Finding underlying connections: A fast graph-based method for link analysis and collaboration queries, ICML-03 |
Mike Cafarella |
| Oct 22 |
Kempe, Kleinberg & Tardos, Maximizing the spread of influence through a social network, KDD-03 |
David Kempe |
| Oct 29 |
McCallum, Efficiently inducing features of conditional random fields, UAI-03 |
Jeff Bilmes |
| Nov 5 |
Guestrin, Koller, Gearhart & Kanodia, Generalizing plans to new environments in relational MDPs, IJCAI-03 |
Mausam |
| Nov 12 |
Pasula, Marthi, Milch, Russell & Shpitser, Identity uncertainty and citation matching, NIPS-02. (See also this follow-up paper.) |
Lin Liao |
| Nov 19 |
Popescul & Ungar, Structural logistic regression for link analysis, KDD-03 MRDM Wkshp |
Deepak Verma |
| Nov 26 |
No seminar (Thanksgiving) |
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| Dec 3 |
McCallum & Wellner, Toward conditional models of identity uncertainty with application to proper noun coreference, IJCAI-03 IIW Wkshp McCallum & Jensen, A note on the unification of information extraction and data mining using conditional-probability, relational models, IJCAI-03 SRL Wkshp |
Parag |
| Dec 10 |
Hofmann, Collaborative filtering via Gaussian probabilistic latent semantic analysis, SIGIR-03 |
Daniel Lowd |
Suggested Papers
- Dhillon, Mallela & Modha, Information-theoretic co-clustering, KDD-03.
- Guestrin, Koller, Gearhart & Kanodia, Generalizing plans to new environments in relational MDPs, IJCAI-03.
- Kempe, Kleinberg & Tardos, Maximizing the spread of influence through a social network, KDD-03.
- Kubica, Moore, Cohn & Schneider, Finding underlying connections: A fast graph-based method for link analysis and collaboration queries, ICML-03.
- McCallum, Efficiently inducing features of conditional random fields, UAI-03.
- McCallum & Wellner, Toward conditional models of identity uncertainty with application to proper noun coreference, IJCAI-03 IIW Wkshp.
- McCallum & Jensen, A note on the unification of information extraction and data mining using conditional-probability, relational models, IJCAI-03 SRL Wkshp.
- Neville & Jensen, Collective classification with relational dependency networks, KDD-03 MRDM Wkshp.
- Oates, Doshi & Huang, Estimating maximum likelihood parameters for stochastic context-free graph grammars, ILP-03.
- Pasula, Marthi, Milch, Russell & Shpitser, Identity uncertainty and citation matching, NIPS-02. (See also this follow-up paper.)
- Perlich & Provost, Aggregation-based feature invention and relational concept classes, KDD-03.
- Poole, First-order probabilistic inference, IJCAI-03.
- Popescul & Ungar, Structural logistic regression for link analysis, KDD-03 MRDM Wkshp.
- Puech & Muggleton, A comparison of stochastic logic programs and Bayesian logic programs, IJCAI-03 SRL Wkshp.
- Segal, Pe'er, Regev, Koller & Friedman, Learning module networks, UAI-03.
- Taskar, Wong & Koller, Learning on the test data: Leveraging 'unseen' features, ICML-03.
- Zhou, Sato & Hasida, Toward a high-performance system for symbolic and statistical modeling, IJCAI-03 SRL Wkshp.
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Department of Computer Science & Engineering
University of Washington
Box 352350
Seattle, WA 98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX
[comments to pedrod]
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