AFLB

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(Recently Seen on Arxiv)
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* [http://arxiv.org/abs/1005.5462 (1005.5462) On the clustering aspect of nonnegative matrix factorization]. Andri Mirzal, Masashi Furukawa
* [http://arxiv.org/abs/1005.5462 (1005.5462) On the clustering aspect of nonnegative matrix factorization]. Andri Mirzal, Masashi Furukawa
* [http://arxiv.org/abs/1005.5513 (1005.5513) Almost Optimal Unrestricted Fast Johnson-Lindenstrauss Transform]. Nir Ailon, Edo Liberty
* [http://arxiv.org/abs/1005.5513 (1005.5513) Almost Optimal Unrestricted Fast Johnson-Lindenstrauss Transform]. Nir Ailon, Edo Liberty
 +
* [http://arxiv.org/abs/1006.1288 (1006.1288) Regression on fixed-rank positive semidefinite matrices: a Riemannian approach]. Gilles Meyer, Silvere Bonnabel, Rodolphe Sepulchre
* http://eccc.hpi-web.de/report/2010/072
* http://eccc.hpi-web.de/report/2010/072

Revision as of 04:03, 8 June 2010

The Algorithms For Lunch Bunch

Mon/Thu @ 1pm (starting May 20)

Venue: the graphics annex


Contents

Readings

Schedule

Date Topic Reading
May 20 Posets Chapter 1: nice proof of Dilworth's theorem - also look at the exercises. Useful discussion of representation of posets here.
May 24 Lattices: definition, semilattices, complete lattices, closure systems Chapter 2 (I'd like to understand the point of closure systems better)
May 27 Distributive and modular lattices Chapter 8; Chapter 9. And a question to ponder. Try not to get too distracted by the discussion of varieties and prime ideals in Chapter 8, since for finite lattices, this is all quite unnecessary.
Jun 7 Modular lattices Chapter 9
Jun 21

Other Topics

Potential topics: (add your name to vote (as many topics as you like))

Current top choices (at least two votes)

  • (Avishek, Chris, Piyush) algorithmic game theory (from this book)
  • (Piyush, Avishek, Jeff, Josh) concentration inequalities for random variables
  • (Jeff, Chris, Josh, Piyush) uncertainty in spatial data (We would follow a series of (mainly) recent papers spanning areas from Computational Geometry, databases, machine learning, to statistics. We would start with models and then move on to specific applications where they are used. We will encounter many interesting open questions. If I get more votes or upon request, I will sketch a paper list in more detail)


General topics

  • (Chris) quantum computing (possibly from these notes)

Complexity Theory

Analysis Tools

Geometry

Miscellaneous

Papers for discussion

Recently Seen on Arxiv

STOC 2010

Add papers here that you found interesting (and link to full version if available)

  • Efficiently Learning Mixtures of Two Gaussians. Adam Tauman Kalai (Microsoft), Ankur Moitra (MIT), and Gregory Valiant (UC Berkeley)
  • Measuring Independence of Datasets. Vladimir Braverman and Rafail Ostrovsky (UCLA)
  • On the Geometry of Differential Privacy. Moritz Hardt (Princeton University) and Kunal Talwar (Microsoft Research)
  • Weighted Geometric Set Cover via Quasi-Uniform Sampling. Kasturi Varadarajan (University of Iowa)
  • A Sparse Johnson-Lindenstrauss Transform. Anirban Dasgupta and Ravi Kumar and Tamas Sarlos (Yahoo! Research)

Other Papers

Previous Semesters

Contact

If you are interested in giving a talk at AFLB or have questions, please feel free to send a mail to moeller@cs.utah.edu, praman@cs.utah.edu or avishek@cs.utah.edu. If you are planning to give a talk, we would really appreciate if you have an abstract ready a week before the talk is scheduled.

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