The STOC workshop on Algorithms with Predictions was on Friday, and I thought it went really well! I can't speak for my talk, but the other 3 talks (Tim Roughgarden, Edith Cohen, Ravi Kumar) were fantastic and inspiring, and I really recommend them for anyone with an interest in "Beyond Worst-Case Analysis".

The talks are all on Youtube. And the workshop page is full of useful links and information.

## Saturday, June 27, 2020

## Thursday, June 25, 2020

### Writing Code for a Paper : A Note to Students

This post both relates to some of the stuff I'll be presenting at Friday's STOC workshop on Algorithms with Predictions, but is also for future students in my classes, who sometimes wonder why I have them write code on theory material. (Somehow this might be a theme for a lecture in my grad class next semester, so maybe these are a first attempt at notes for the lecture. Comments and suggestions welcome.)

Some of the work I'm doing is looking at how queueing systems perform with various sorts of predictions on the times the jobs take. This particular work I'm doing on my own. (While most of my research has been and is with collaborators, and it's one of the things I enjoy about computer science -- we're a very collaborative field!, which seems to surprise many people -- I still sometimes like to do research projects on my own. I've looked at queueing systems since my PhD thesis, and it's a bit outside the research interest of most of my collaborator pool, and it's "fun" sometimes to do my own thing. The reason why "fun" is in quotes is described below.)

Often in my work in queueing I'm looking at mean-field limits (meant to model infinite systems of queues, which provides a good approximation for large finite systems under reasonable assumptions), where I can derive families of differential equations describing the system behavior. I can also simulate the large finite system directly, and make sure the results match. I generally do this for all of these types of papers.

Now the numbers I get from simulating the system directly and from simulating the differential equations should match (say within 1% or so). If they don't, something is wrong. In an effort to avoid wrongness, I won't consider the paper ready for outside consumption until I get a match. Unfortunately, there are three ways things can go wrong.

1. My simulation code for the queueing system might have bugs.

2. My code to evaluate the differential equations might have bugs.

3. My equations themselves might have bugs.

And I find there are two main categories of bugs. Sometimes the bugs are simple/standard coding mistakes -- I'm off by 1 on an index, or I cut and paste and forget to change an i++ to a j++ in one my double loops, or I type x instead of a y. Usually it's pretty easy to find these things, although I've had times where a hidden typo took hours to find. But sometimes the bug is a thinking mistake -- I've forgotten a subcase and so my equations aren't complete (and so my code evaluating the equations won't give the right answer), or I've not handled a subcase correctly in my simulation. That type usually takes longer.

Usually, the first time through, most all of these types of bugs happen -- my math is off, I've typed some stuff wrong, it can all happen. And then, like coders everywhere, I go through and fix it. And it's painful. Sometimes everything goes right, a quick check or two and everything works. For more complicated stuff, it's more time figuring out what went wrong than setting up the code to begin with. And being the personality type to not let things sit, that can mean late nights figuring out what went wrong.

For my talk this week, there was one last problem I wanted to include, which meant finally taking the model and writing the equations and code. I didn't even need it for the talk, but it's also the last bit before I put a paper draft on arxiv, so taking advantage of a deadline, I figured now was the time. Which means the last 2 days, I've spent many hours (and a late night) trying to remove the disagreements.

On the plus side, when everything finally works, it's a wonderful feeling. And it always makes me feel better when I have worked to verify my math this way; this time, what kept me up well past midnight and took several hours to track down was actually a boundary case I had left out of the equations. (I had looked at the equations over and over again without noticing I had left out the subcase; I had to step through numbers from the differential equations one time step at a time to track down what was missing, and then the numbers told me what I had done wrong.)

On the down side, it's work, and debugging is never particularly fun.

For students out there, maybe I'm just explaining that I understand the pain that I am putting you through. You may wonder why I have you do simulations that take a few hours if you do them well, but days if you don't think through the best approach. But using programming and theory together can be powerful; it's helped me countless times in my research.

(Related: on theory and experiments that I've written on before, along with a viewpoint by Jeffrey Ullman.)

Some of the work I'm doing is looking at how queueing systems perform with various sorts of predictions on the times the jobs take. This particular work I'm doing on my own. (While most of my research has been and is with collaborators, and it's one of the things I enjoy about computer science -- we're a very collaborative field!, which seems to surprise many people -- I still sometimes like to do research projects on my own. I've looked at queueing systems since my PhD thesis, and it's a bit outside the research interest of most of my collaborator pool, and it's "fun" sometimes to do my own thing. The reason why "fun" is in quotes is described below.)

Often in my work in queueing I'm looking at mean-field limits (meant to model infinite systems of queues, which provides a good approximation for large finite systems under reasonable assumptions), where I can derive families of differential equations describing the system behavior. I can also simulate the large finite system directly, and make sure the results match. I generally do this for all of these types of papers.

Now the numbers I get from simulating the system directly and from simulating the differential equations should match (say within 1% or so). If they don't, something is wrong. In an effort to avoid wrongness, I won't consider the paper ready for outside consumption until I get a match. Unfortunately, there are three ways things can go wrong.

1. My simulation code for the queueing system might have bugs.

2. My code to evaluate the differential equations might have bugs.

3. My equations themselves might have bugs.

And I find there are two main categories of bugs. Sometimes the bugs are simple/standard coding mistakes -- I'm off by 1 on an index, or I cut and paste and forget to change an i++ to a j++ in one my double loops, or I type x instead of a y. Usually it's pretty easy to find these things, although I've had times where a hidden typo took hours to find. But sometimes the bug is a thinking mistake -- I've forgotten a subcase and so my equations aren't complete (and so my code evaluating the equations won't give the right answer), or I've not handled a subcase correctly in my simulation. That type usually takes longer.

Usually, the first time through, most all of these types of bugs happen -- my math is off, I've typed some stuff wrong, it can all happen. And then, like coders everywhere, I go through and fix it. And it's painful. Sometimes everything goes right, a quick check or two and everything works. For more complicated stuff, it's more time figuring out what went wrong than setting up the code to begin with. And being the personality type to not let things sit, that can mean late nights figuring out what went wrong.

For my talk this week, there was one last problem I wanted to include, which meant finally taking the model and writing the equations and code. I didn't even need it for the talk, but it's also the last bit before I put a paper draft on arxiv, so taking advantage of a deadline, I figured now was the time. Which means the last 2 days, I've spent many hours (and a late night) trying to remove the disagreements.

On the plus side, when everything finally works, it's a wonderful feeling. And it always makes me feel better when I have worked to verify my math this way; this time, what kept me up well past midnight and took several hours to track down was actually a boundary case I had left out of the equations. (I had looked at the equations over and over again without noticing I had left out the subcase; I had to step through numbers from the differential equations one time step at a time to track down what was missing, and then the numbers told me what I had done wrong.)

On the down side, it's work, and debugging is never particularly fun.

For students out there, maybe I'm just explaining that I understand the pain that I am putting you through. You may wonder why I have you do simulations that take a few hours if you do them well, but days if you don't think through the best approach. But using programming and theory together can be powerful; it's helped me countless times in my research.

(Related: on theory and experiments that I've written on before, along with a viewpoint by Jeffrey Ullman.)

## Wednesday, June 17, 2020

### Algorithms with Predictions: Survey and Workshop

There's a whole new, interesting theory trend -- Algorithms with Predictions. The idea, spurred by advances in machine learning, is that you assume you have predictor that tells you something about your input. For example, in caching, you might have a prediction of when the item you are currently accessing will be next accessed. Of course, machine learning predictions aren't perfect. Still, you'd like to use this prediction to improve your caching algorithm, but from the theory side, we'd like provable statements. For example, you could say, if my prediction is THIS good (e.g., the error is bounded under some metric), then my caching performance will correspondingly be at least THIS good (e.g., performance bounded in some way).

If you haven't seen the burgeoning spread of this line of work and are interested, you're in luck. First, Sergei Vassilvitskii and I have written a brief survey that's now on the arxiv. We had written it for a collection Tim Roughgarden is organizing on Beyond Worst-Case Analysis (that we thought we be out by now, and should be out from the publisher soon-ish), but we've gone ahead and put a version on the arxiv to make it available. The area is moving fast, so there are already many new results -- we hope to update the "survey" with new material as the area grows.

Second, one of the STOC'20 Workshops will be on Algorithms with Predictions. It will be on Friday from 1-4pm, with speakers Tim Roughgarden, Edith Cohen, Ravi Kumar, and me. I'll be talking about some of my recent work (in submission) on queues with predictions, and partitioned learned Bloom filters. (Arxiv papers are here, here, and here, but maybe you want to see the talk first.) I'll also do a blog post on partitioned learned Bloom filters in the near future.

If you haven't seen the burgeoning spread of this line of work and are interested, you're in luck. First, Sergei Vassilvitskii and I have written a brief survey that's now on the arxiv. We had written it for a collection Tim Roughgarden is organizing on Beyond Worst-Case Analysis (that we thought we be out by now, and should be out from the publisher soon-ish), but we've gone ahead and put a version on the arxiv to make it available. The area is moving fast, so there are already many new results -- we hope to update the "survey" with new material as the area grows.

Second, one of the STOC'20 Workshops will be on Algorithms with Predictions. It will be on Friday from 1-4pm, with speakers Tim Roughgarden, Edith Cohen, Ravi Kumar, and me. I'll be talking about some of my recent work (in submission) on queues with predictions, and partitioned learned Bloom filters. (Arxiv papers are here, here, and here, but maybe you want to see the talk first.) I'll also do a blog post on partitioned learned Bloom filters in the near future.

## Saturday, June 06, 2020

### CATCS Visioning Workshop

Reposting an important call -- these events have had big impact in the past!

The CATCS will be hosting a virtual

**“Visioning Workshop”**the**week of****July 20**in order to identify broad research themes within theoretical computer science (TCS) that have potential for a major impact in the future. The goals are similar to the workshop of the same name in 2008: to package these themes in a way that can be consumed by the general public, which we would deliver primarily to the Computing Community Consortium and others (e.g. funding agencies) to help them advocate for TCS.
While participation in the workshop is primarily through invitation, we have a few slots available for the broader community. If you are interested in participating, please see details of the application process below. The workshop will be organized according to area-wise breakout groups. Each breakout group will have 1-2 leads. Breakout groups will meet for 4-5 hours spread across several days and will be tasked with brainstorming ideas and preparing materials related to their topic. Leads are further expected to participate in plenary sessions held on Monday July 20 and Friday July 24 (4-5 hrs of additional time) where these materials will be discussed.

If you are interested in participating in the workshop, please fill out this Google form by

**Monday June 15**. On this form, applicants are asked to contribute one or two major results in the last 10 years whose significance can be explained in layperson terms, and one or two major challenges for theory whose significance can be explained in layperson terms. These descriptions can be very brief. We will just use them to select participants and create breakout groups.
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