COMP 480/580 Probabilistic Algorithms and Data Structures
- Instructor: Anshumali Shrivastava (anshumali AT rice)
- TA: TBD
- TA Office Hours: TBD
- Class Timings: Tue/Thu 01:00PM - 02:15PM
- Location: HRZ 210
- Instructor Office Hours: Tue 2:15PM - 3:15PM DH 3118 (Anshu)
List of sample project topics link
Please sign up for lectures you want to scribe at excel
This course will be ideal for someone wanting to build a strong foundation in the theory and practice of algorithms for
processing Big-Data. We will discuss advanced data structures and algorithms going beyond deterministic setting and emphasize the role
of randomness in getting significant, often exponential, improvements in computations and memory.
COMP 182 or equivalent required. COMP 382 is useful but not required. Basic Knowledge of
Probability. Knowledge of programming is required. Capability to manipulate primitive data structures such as arrays, list, etc. will
be needed for assignments.
Most of the materials (scribes and slides) needed will be posted on this website.
Some of the materials are fairly new and textbook is yet to be written.
A Nice Book to have is "Probability and Computing: Randomized Algorithms and Probabilistic Analysis
" by Michael Mitzenmacher and Eli Upfal.
- 01/14 : Introduction, Logistics, Mark and Re-capture Estimation.Scribe Slide
- 01/16 : Brief Pseudo Randomness, Universal Hashing, Chaining, and Linear Probing.Slide
- 01/21 : Compressed Cache and Bloom Filters. (Why caching does not kill your memory) Slide
- 01/23 : Markov, Chebyshev's, and Chernoff Bounds. Slide
- 01/28 : Analysis of Hashing, Chaining and Probing. Slide
- 01/30 : Resizing Hash Tables: Consistent Hashing and balanced allocations.(The idea behind Akamai Technologies)
- 02/04 : SPOCA: A Stateless, Proportional, Optimally-Consistent Addressing Algorithm. (Best paper in USENIX 2011)
- 02/06 : Introduction to Stream Computing and Reservoir Sampling. (Algorithms at the Edges)
- 02/11 : Stream Estimation 1: Count-Min Sketch (Generalized Bloom Filters)
- 02/13 : Spring Break.
- 02/18 : Stream Estimation 2: Count-Sketches
- 02/20 : Stream Estimation 3: Count Distinct and Norm Estimation on Streams.
- 02/25 : Minwise Hashing, Near-Duplicate Detection and LSH 1. (What is "hash function" in this NY-Times article)
- 02/27 : Minwise Hashing, Near-Duplicate Detection and LSH 2.
- 03/03 : More LSH (Eliminate Pairwise Comparisons)
- 03/05 : Basic Sampling. (Beyond Random Sampling)
- 03/10 : Sampling Continued
- 03/12 : LSH as Computationally Efficient Importance Samplers. (Adaptive Sampling at the Cost of Random Sampling)
- 03/17 : Spring Break.
- 03/19 : Spring Break.
- 03/24 : In Class Mid-Term Exam.
- 03/26 : Markov Chains, Stationary distributions, MCMC 1 (Sampling in Complex Spaces).
- 03/31 : Markov Chains, Stationary distributions, MCMC 2
- 04/02 : Markov Chains, Stationary distributions, MCMC 3
- 04/07 : Markov Chains, Stationary distributions, MCMC 4.
- 04/09 : Markov Chains, Stationary distributions, MCMC 5.
- 04/14 : Estimation on Graphs
- 04/16 : Randomized Routing
- 04/21 : ACE and RACE Density Estimation (Analytics over Edge)
- 04/23 : Slack or Special Topics
Students with Disability
If you have a documented disability that may affect academic performance, you should: 1) make sure this documentation is on file with Disability Support Services (Allen Center, Room 111 / email@example.com / x5841) to determine the accommodations you need; and 2) meet with me to discuss your accommodation needs.