CS395T: Sublinear Algorithms (Fall 2020)

Logistics: Tue/Thu 2:00 - 3:30
Online via Zoom
Unique Number: 51450
Course web page: http://www.cs.utexas.edu/~ecprice/courses/sublinear/
Professor: Eric Price
Email: ecprice@cs.utexas.edu
Office: GDC 4.510
Office Hours: Thursday 3:30 - 4:30
TA: Shivam Gupta
Email: shivamgupta@utexas.edu
Office Hours: TBA
Content: This graduate course will study algorithms that can process very large data sets. In particular, we will consider algorithms for:
  • Data streams, where you don't have enough space to store all the data being generated.
  • Property testing, where you don't have enough time to look at all the data.
  • Compressed sensing, where you don't have enough measurement capacity to observe all the data.

Most of the class will focus on lectures and problem sets. Another key component -- worth 30% of the grade, and correspondingly important -- is a final project, wherein the student will dive in depth into the cutting edge of research.

Useful References: The 2014 and 2016 versions of the class contain useful notes. Other similar courses are linked from here.
Problem Sets: Problem sets will be due almost every week at the beginning of class. Typewritten solutions are preferred.
Lectures:
DateTopicScribe notesVideoHW
August 27 Introduction; basic uniformity testing Scribe notes Zoom recording
September 1 Distinct elements counting Scribe notes Zoom recording HW 1 due Sep 8
September 3 Distinct elements counting II: limited independence and turnstile Scribe notes Zoom recording
September 8 Distinct elements counting II: limited independence and turnstile Scribe notes Zoom recording HW 2 due Sep 15
September 10 Lower bounds; concentration inequalities Scribe notes Zoom recording
September 15 Quantile estimation by sampling Scribe notes Zoom recording HW 3 due Sep 22
September 17 Quantile estimation Scribe notes Zoom recording
September 22 Heavy Hitters: FrequentElements and Count-Min Sketch Book chapter
Scribe notes		 Zoom recording HW 4 due October 1
September 24 On estimation of symmetric random variables Scribe notes Zoom recording
September 29 Count-Sketch Scribe notes Zoom recording
October 1 Subgaussian and subgamma random variables Zoom recording HW 5 due October 13
October 6 Subgamma random variables and covering numbers Scribe notes Zoom recording
October 8 The RIP and compressed sensing Scribe notes Zoom recording
October 13 Iterative hard thresholding, model-based compressive sensing Scribe notes Zoom recording HW 6 due October 20
October 15 L1 minimization, packing & covering numbers Scribe notes Zoom recording
October 20 Lower bounds for compressed sensing Scribe notes Zoom recording
October 22 Adaptive sparse recovery Scribe notes Zoom recording HW 7 due October 29
October 27 Fourier RIP Slides
Scribe notes		 Zoom recording
October 29 RIP-1 and SSMP Scribe notes Zoom recording
November 3 Graph Sketching Scribe notes Zoom recording
November 5 More Graph Sketching Scribe notes Zoom recording HW 8 due November 12
November 10 Uniformity testing Zoom recording
November 12 More distribution testing Zoom recording HW 9 due November 19
November 17 Independence testing, start Chow-Liu Zoom recording
November 19 Finish Chow-Liu, property testing of monotonicity Zoom recording
November 24 Property testing: 2d images and graphs Zoom recording
The outline for the course is as follows:
  • Background: Concentration inequalities and Johnson-Lindenstrauss
  • Streaming algorithms:
    • Distinct elements counting
    • Heavy hitters
    • Graph sketching
    • Coresets
  • Compressed sensing:
    • RIP, iterative hard thresholding
    • L1 minimization
    • Model-based compressed sensing
    • Lower bounds for compressed sensing
    • Adaptivity
    • Sparse Fourier transforms
  • Property testing:
    • Distribution testing: uniformity, independence
    • Testing monotonicity of lines and grids
    • Testing graphs
Prerequisites: Mathematical maturity and comfort with undergraduate algorithms and basic probability. Ideally also familiarity with linear algebra.
Grading: 50%: Homework
30%: Final project
20%: Scribing lectures
Scribing: In each class, two students will be assigned to take notes. These notes should be written up in a standard LaTeX format before the next class.
Homework
policy:		 There will be a homework assignment every week.

Collaboration policy: You are encouraged to collaborate on homework. However, you must write up your own solutions. You should also state the names of those you collaborated with on the first page of your submission.

Late policy: Each student gets 3 free late days for homeworks during the semester. However, use of these days must be announced in advance (i.e., the instructors must be notified before the assignment is due that the late days will be used).

Final project: In lieu of a final exam, students will perform final projects. These may be done individually or in groups of 2-3. An ideal final project would perform a piece of original research in a topic related to the course. Failing that, one may perform a literature survey covering several research papers in the field.

Students will present their results to the class during the last week of classes. The final paper will be due on the scheduled final exam day.

Students with
Disabilites: 		Any student with a documented disability (physical or cognitive) who requires academic accommodations should contact the Services for Students with Disabilities area of the Office of the Dean of Students at 471-6259 (voice) or 471-4641 (TTY for users who are deaf or hard of hearing) as soon as possible to request an official letter outlining authorized accommodations.
Additional Class Policies You should read CS Department Code of Conduct. The policies described there will be followed in this class.