CSE 694: Probabilistic Analysis and Randomized Algorithms (Spring 2011)

Presentation Schedule (Each presentation is 50 minutes long).

• Tue Apr 19. Capen 260. 9am. Gaurav
• Tue Apr 19. Capen 260. 10am. Utkarsh
• Thu Apr 21. Capen 260. 9am. Swapnooneel
• Thu Apr 21. Capen 260. 10am. Prakash
• Tue Apr 26. Capen 260. 9:30am. Branislav
• Tue Apr 26. Capen 260. 10am. Yingjie
• Wed Apr 27. Bell 224. 9am. Liang
• Wed Apr 27. Bell 224. 10am. Caiming
• Thu Apr 28. Capen 260. 9:30am. Andrew
• Thu Apr 28. Capen 260. Last homework assignment 5 due.

Logistics

• Student presentations are in the last 2 weeks (Apr 19, 21, 26, 28). We will likely have to find another room for the presentation because we may need a bit more time than the usual 9:30--10:50.
• Requirements:
  • Please prepare slides.
  • It is preferable if you use LaTeX Beamer. PPT is ok but it looks very ugly when there's heavy math formulas. Here is a set of slides from my first lecture prepared using Beamer, which you should make use of as a template. Note the following:
    • Type make will produce main.pdf which could be viewed with acroread, xpdf in Linux or preview under MacOSX. In Linux, xpdf is preferred because it refreshes automatically.
    • To produce the final slides, do make twice in a row, or pdflatex main twice in a row. This makes sure the the table of content and cross-references are correctly constructed and
    • If you do include graphics, use the includegraphics command as in the template. Most graphic types are ok: pdf, jpeg, png, etc., except for eps/ps which you'll have to convert to pdf before inclusion.
  • Please follow roughly the following outline in the presentation:
    • What's the problem
    • What are the related works to date (of the paper)
    • What's the main line of attack, i.e. outline the key lemmas and theorems (before proving them)
    • Try to explain in English the main ideas behind the lemmas and theorems
    • Finally, prove some of them as time allows. Skip calculus or elementary details.
    • Let us know what you think might be next in terms of open problems, things you might try, etc.

The following list of presentation choices will be updated.

Coupon Collector

• Foata, Dominique; Zeilberger, Doron The collector's brotherhood problem using the Newman-Shepp symbolic method. Dedicated to the memory of Gian-Carlo Rota. Algebra Universalis 49 (2003), no. 4, 387–395.
• D. Newman and L. Shepp, (1960) The Double Dixie Cup Problem, Amer. Math. Monthly, 67 (1960), pp. 58–61.
• Adler, I., Oren, S., and Ross, S. (2003). The Coupon Collector's Problem Revisited, J. Appl. Probab., 40, no. 2, 513-518.

Balls into Bins

• Yossi Azar, Andrei Z. Broder, Anna R. Karlin, Eli Upfal: Balanced Allocations. SIAM J. Comput. 29(1): 180-200 (1999)
• Petra Berenbrink, Artur Czumaj, Angelika Steger, Berthold Vöcking: Balanced Allocations: The Heavily Loaded Case. SIAM J. Comput. 35(6): 1350-1385 (2006) [ pdf ]
• B. Vöcking, How asymetry helps load balancing, J. ACM, 50 (2003), pp. 568–589. [ pdf ]
• Kunal Talwar, Udi Wieder Balanced allocations: the weighted case. STOC'07—Proceedings of the 39th Annual ACM Symposium on Theory of Computing, 256–265, ACM, New York, 2007

Networking

• Micah Adler. 2005. Trade-offs in probabilistic packet marking for IP traceback. J. ACM 52, 2 (March 2005), 217-244.
• Masoud Sharif, Babak Hassibi: Delay Considerations for Opportunistic Scheduling in Broadcast Fading Channels. IEEE Transactions on Wireless Communications 6(9): 3353-3363 (2007)

Hashing

• [Yingjie will present]A. Pagh and F. Rodler. Cuckoo hashing. Journal of Algorithms, 51(2):122-144, 2004. [ pdf ]
• Yuriy Arbitman, Moni Naor, and Gil Segev. 2009. De-amortized Cuckoo Hashing: Provable Worst-Case Performance and Experimental Results. In Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I (ICALP '09) [ pdf ]
• M. Mitzenmacher and S. Vadhan. Why simple hash functions work: exploiting the entropy in a data stream. In Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 746-755, 2008 [ pdf ]
• Michael Mitzenmacher, Some Open Questions Related to Cuckoo Hashing

Complexity

• [Andrew will present] Robin A. Moser and Gábor Tardos. 2010. A constructive proof of the general lovász local lemma. J. ACM 57, 2, Article 11 (February 2010), 15 pages. (Best paper award at STOC 2009)

Cryptography and Security

• [Branislav will present] Benny Chor, Amos Fiat, Moni Naor, Benny Pinkas: Tracing traitors. IEEE Transactions on Information Theory 46(3): 893-910 (2000)
• [Liang will present] Gábor Tardos: Optimal probabilistic fingerprint codes. J. ACM 55(2): (2008)
• Dalit Naor, Moni Naor, Jeffery Lotspiech: Revocation and Tracing Schemes for Stateless Receivers. CRYPTO 2001: 41-62

Learning

• Sanjoy Dasgupta. Learning mixtures of Gaussians. Fortieth Annual IEEE Symposium on Foundations of Computer Science (FOCS), 1999. (See also these slides for historical facts.)

Data Streaming

• Cynthia Dwork, Moni Naor, Toniann Pitassi, Guy N. Rothblum, Sergey Yekhanin: Pan-Private Streaming Algorithms. ICS 2010: 66-80 [ pdf ]
• [Utkarsh will present]M. Charikar, K. Chen and M. Farach-Colton, "Finding Frequent Items in Data Streams." In ICALP 02. [The Count-Sketch data structure.]
• [Gaurav will present] Graham Cormode, S. Muthukrishnan: An improved data stream summary: the count-min sketch and its applications. J. Algorithms 55(1): 58-75 (2005)
• [Swapnooneel will present] Ziv Bar-Yossef, T. S. Jayram, Ravi Kumar, D. Sivakumar, and Luca Trevisan. 2002. Counting Distinct Elements in a Data Stream. In Proceedings of the 6th International Workshop on Randomization and Approximation Techniques (RANDOM '02)
• N. Alon, Y. Matias and M. Szegedy, "The space complexity of approximating the frequency moments." In JCSS 99 (prelim. version STOC 96 -- the classic paper in data stream).
• A. Chakrabarti, G. Cormode and A. Mcgregor, "A Near-Optimal Algorithm for Computing the Entropy of a Stream." In SODA 07.
• [Prakash will present] Radu Berinde, Piotr Indyk, Graham Cormode, Martin J. Strauss: Space-optimal heavy hitters with strong error bounds. ACM Trans. Database Syst. 35(4): 26 (2010). [ pdf ]

Privacy Preserving Algorithms

• Cynthia Dwork, Moni Naor, Toniann Pitassi, Guy N. Rothblum: Differential privacy under continual observation. STOC 2010: 715-724 [ pdf ]
• Cynthia Dwork: Differential Privacy in New Settings. SODA 2010: 174-183. [ pdf ]
• Cynthia Dwork, Guy N. Rothblum, Salil P. Vadhan: Boosting and Differential Privacy. FOCS 2010: 51-60 [ pdf ]
• Cynthia Dwork: Differential Privacy: A Survey of Results. TAMC 2008: 1-19 [ pdf ]
• Michael T. Goodrich Randomized Shellsort: A Simple Oblivious Sorting Algorithm. 1262-1277 2010 SODA [ pdf ]

Databases

• Florin Rusu, Alin Dobra: Sketches for size of join estimation. ACM Trans. Database Syst. 33(3): (2008)
• Florin Rusu, Alin Dobra: Sketching Sampled Data Streams. ICDE 2009: 381-392
• [Caiming will present] Dimitris Achlioptas. 2003. Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J. Comput. Syst. Sci. 66, 4 (June 2003), 671-687.(also ACM PODS 2001).