Thursday, June 22, 2017

TheoryFest Day 3: "I forgot to talk about Kant"

The above is an actual quote from Oded Goldreich in his hilarious speech accepting the Knuth Prize for 2017. This speech was highly anticipated, because most of us have spent years reading and marvelling at Oded's opinions (he addressed the elephant in the room very promptly)

As the title suggests, there was a heavy dose of philosophy, with quotes from Kant, Weber, Frankl, and MacIntyre. He also gave a brief technical exposition of new developments in interactive proofs starting from the "IP for Muggles" paper of Goldwasser, Kalai and Rothblum. This topic is quite dear to me right now, given that my student Samira Daruki just defended a thesis that is at least partly about interactive proofs where the verifier is extremely weak (aka streaming). I'll have more to say about this later.

One can only hope the video of Oded's talk will be available online soon: it's can't miss-TV :).

(I'm keeping these posts short in the hope that I'll actually write them. The danger in longer posts is that they never get written). 

TheoryFest Day 3: Panels and Lunches

(for various reasons, I don't have wifi access at my Airbnb, so my posts are delayed. But it's not like you're hanging on my every word...... are you?)

Day 3 started off with a panel moderated by Anna Karlin, starring Cynthia Dwork, Russell Impagliazzo, Ankur Moitra, Tim Roughgarden, Dan Spielman and Andy Yao. I personally like panels to have a bit of an edge and controversy, and there wasn't that much of it here, but there was some good discussion about various aspects of TCS, as well as some good advice for the young'uns in the audience. Some of the highlights:

  • Andy Yao thinks a working quantum computing device is imminent, but one that we could solve Rubik's cube puzzles on might take a while longer. 
  • Cynthia Dwork made a strong argument for the power of theoryCS to define problems clearly and rigorously even for messy concepts like privacy and fairness. 
  • Ankur Moitra made a point that I think should be made more often: that modeling and algorithm design are fundamentally different things even if they might be influenced by one another. And it's important not to confuse the two. 
  • Dan Spielman tried to emphasize that even if it might seem like senior people at a conference know what they're talking about, they don't really know. And that's it's ok to be in a constant state of befuddlement. As Anna Karlin put it I think, "We must learn to dance with chaos".
There was lots more that the panel discussed, but these are what stuck in my mind. One pity was that we ran out of time and couldn't have any audience questions. 

As I had mentioned earlier, there were networking lunches for senior (aaaah, I'm a senior now :() and junior people. Never one to let of a chance to pontificate, I had lunch with Sima Hajiaghaei (UVic), Lalla Mouatadid (Toronto), Benjamin Priest (Dartmouth) (ed: you need a website!) and Shravas Rao (NYU) at a nice dumpling place in Chinatown. They won't like me saying this, but I will anyway :) - they all seemed so young and enthusiastic I felt a little old. But we had a great chat, and if you bump into any of them at a theory conference in a future make sure to say hi :). 

Wednesday, June 21, 2017

TheoryFest Day 2: Directed Spectral methods

There was an interesting talk on developing spectral tools for directed graphs. The magic of spectral methods for undirected graphs comes from the interpretation of the graph as a Markov chain, and then using spectral properties of the Laplacian (which is symmetric and positive semidefinite) to reason about a host of properties like conductance, mixing times, sparsification, and others.

But for directed graphs none of this works as stated. The Laplacian is no longer symmetric (goodbye real eigenvalues) and even if we symmetrize it it may not be positive definite (goodbye nonnegative eignenvalues). One of the key tricks that this paper exploits (and relates to work by Fan Chung on Cheeger inequalities for directed graphs) is the following:
Suppose our directed graph is Eulerian, which means that each vertex has the same incoming and outgoing degree. Then if we construct the associated Laplacian $L$ and symmetrize it as $L' = (L + L^T)/2$, then the resulting symmetric matrix $L'$ is positive definite!
This turns out to be a key ingredient of designing efficient algorithms for estimating spectral quantities on directed graphs, and is a trick worth remembering.

TheoryFest: Avi Wigderson's plenary talk.

The plenary talk on Tuesday morning was by Avi Wigderson, on the nature of TCS.

Now if you haven't attended an Avi Wigderson plenary before, you should imagine yourself lying in a nice warm tub of water with soothing bath salts massaging your aching limbs. Avi's talks are balm for the poor persecuted theoretician who comes to STOC to remember that there people in the world who don't insist on demanding practical value for every darn theorem you prove.

His talk was not technical, and not even particularly informative for anyone who's been paying attention to TCS the last 40 years. What it was, was a love song to TCS, sung by a bard with +5 charisma who makes you feel like you have purpose and meaning in your life once again.

His slides will be up soon, and you can see them for yourself. But if there's anyone who can make the argument for the universality, depth and reach of TCS at its highest conceptual level, it's Avi. I'll link them when they're available and if you care at all about the field, you should take a look.

TheoryFest: Attack of the talks

Day 2 at TheoryFest, and people are still attending talks. Ok maybe I shouldn't be that surprised. But it's kind of nice to see anyway. The lounge area is deserted during the sessions and full during the breaks.

The TheoryFest organizing committee (as represented by Ryan Williams) has organized lunch time meetups for senior and junior people (where junior means grad students and postdocs, not assistant profs — sorry, assistant profs). The idea is that the senior people sign up and the junior people can email them to meet up. It's a nice idea, especially given the feeling that I've heard from people in the past that STOC is an unfriendly place for students, especially those not from high-powered theory places.

One interesting aspect of coming to STOC after a number of years is while I know many the older folk, I don't know most of the students, and so I float around relatively anonymous (it doesn't help that I haven't blogged much in recent months - I've been distracted, people!). It's almost like being at a brand new conference that I've never attended before.

Tuesday, June 20, 2017

TheoryFest: Short Takes

So far, the tutorials appear to have been well attended, The DL tutorial had a full house in a big room, but the other two tutorials did pretty well to. The plenary talks (the reason I'm here) start today and it will be interesting to see what kind of attendance we see.

The business meeting will reveal the official numbers: indications are that they will be quite good especially considering we're not in the US.

Montreal is a nice town. My AirBnB is right next to Chinatown and we're pretty much in the center of everything. The parts I've walked through have this kind of pacing with cul de sacs, pedestrian-only areas, main roads, and parks that feels like well-paced musical composition. 

TheoryFest I: Deep Learning

(ed: I'm guessing you never thought those words would appear together in the same phrase)

Ruslan Salakhutdinov gave a tutorial on deep learning today. Now deep learning is a tricky topic for theory (more on that below), but I thought he did a nice job in his two hours of

  • explaining the basics of how a neural net works and how it's trained, without getting too far into engineering weeds, but also being able to explain important ideas like drop out, SGD, batch normalization and momentum. He skillfully avoided the alphabet soup of architectures in a way that didn't really affect one's understanding (I think). He didn't get too much into RNNs, but I think that was a conscious and fair choice. 
  • Discussing the unsupervised element of DL - autoencoders, RBMs, DBMs, and GANs. Now I have a little bit of an advantage here because we're running a summer reading group on GANs, but I liked his framing here in terms of supervised and unsupervised, as well as the different kind of generative criteria (probabilistic/not, tractable/intractable, explicit/implicit) used to classify the different approaches. 

In a first at STOC (maybe?) the audience got to see a video of an RL system learning to play Doom. It was pretty neat.

Having said that, I'm not exactly the right audience for this kind of talk, since I'm decently familiar with deep learning. What surprised me though was that when I polled people during the breaks. most of the people who attended the tutorial felt the same way. And the common refrain was "We've heard so many faculty canddiates talk about deep learning that we know the basics now"!

So I almost wonder if Russ miscalbrated the level of the audience.

There was also some minor grumbling about the lack of clear open problems. I actually don't fault him for that. I think it might have been useful to expose core ideas for which answers don't exist, and some of these came out in the Q&A.

But let me make a more general observation. Deep learning is a tricky topic for theoreticians to negotiate, for a number of reasons.

  • firstly, I don't think it's even useful to ask the most general form of "what does a neural net DO" questions. Neural nets are very very general (in particular a 2-level neural net can approximate any function). So asking general questions about them is like asking to characterize a Turing machine with no constraints. You can't say much beyond recursive and r.e. I think ther right questions are much more specific. 
  • DL right now is very much an engineering discipline, which is to say that the practice of DL is focused on trying out engineering hacks that appear to get improvements. And these improvements are significant enough that it really doesn't matter why they work. In other words, DL doesn't need theory… at least now. 
  • Even if you don't grant the previous two positions, there's another issue. Descriptions of DL systems feel a lot like experimental physics: "hey we did all of this and it worked this way. Now give us a theory". With the difference that there's no "there" there: there's no fixed Nature that we can design theoretical laws against. Only a gazillion-dimensional highly nonconvex landscape where we don't even try to find a provably high quality answer. 

So I think we're on our own if we want to (or care to) understand the computational power and expressivity of neural networks. It's very interesting, and we're seeing nice results begin to appear, but we should do it because there's interesting theory to be had here, rather than trying to hew to close to actual DL systems.

Sunday, June 18, 2017

Off to TheoryFest 2017

I'm at the airport getting ready to board my flight for Montreal to attend TheoryFest 2017. And much to my amazement, I discover that STOC has its own event mobile app. Who knows, maybe this means that by next decade theory conferences will do double blind review? (ed: stop it, now that's crazy talk!)

Snark aside, I'm very excited to see how the new format for STOC works. This is an experiment, and like all experiments it will take some time to see how the idea plays out. But first impressions matter, and if this year's iteration goes well, that will be good news for the overall plan.

I'm of course excited for the plenary invited talks, but I'm also excited to see the tutorials and workshops. Even theory land is not safe from the invasion of the Huns deep learning and I'm sure there will be standing-room only space at Ruslan Salakhutdinov's tutorial on the foundations of deep learning.

My "coverage" will be a mix of tweeting and blogging. Stay tuned!

Monday, May 01, 2017

Easy memory aides

Certain memory aides are so ... well.. memorable that they stick in your mind exactly the way they should. Here are three that I've heard of:

  • Keeping track of the Baltic states: I think I heard this from +Fernando Pereira - They are alphabetical in order from north to south. So it's Estonia, Latvia and then Lithuania. 
  • Converting between miles and kilometers: This is a particularly geek-friendly mnemonic - If the value in miles is (close to) a Fibonacci number, the value in kilometers is (close to) the next Fibonacci number. This works because the golden ratio is very close to the miles-to-km conversion factor.
  • Keeping track of type-I/type-II errors: I heard this one on twitter the other day, and it goes like this. In the story of the boy who cried wolf, the errors appear in order. 
Things I need memory aides for:
  • Bernstein vs McDiarmid vs Azuma, or different Chernoff bounds in general. 
  • Jensen's inequality, so I don't have to constantly draw the picture of the convex function to derive it.
  • Submodularity vs supermodularity: I know you add the diagonals and compare them, but which way is which???

Wednesday, April 19, 2017

TheoryFest at STOC 2017.

(ed: I can't believe it's been four months since my last post. Granted, I've been posting over at, but still. Time to turn in my theory card...)

One of the things I've been doing is helping out with the STOC TheoryFest this summer. Specifically, I've been on the plenary talks committee that was tasked with identifying interesting papers from other parts of CS and beyond that might be interesting for a STOC audience to hear about. After many months of deliberations, we're finally done! Boaz has more details over at his blog.

Do take a look at the papers. Boaz has a great summary for each paper, as well as links to the sources. You'll see a pretty decent coverage of different topics in CS, including even some theory B !!

But more importantly, please do register for STOC! This year, the organizers are making a real effort to restructure the conference to make it more accessible with a diversity of events like the plenary talks, tutorials, and workshops, and there should be something for everyone. Early registration ends on May 21, so click that link now.

In this day and age, I don't need to emphasize the importance of standing up and being counted. If you've ever grumbled about the old and stultifying format of STOC, this is your chance to vote with your feet. A big success for TheoryFest 2017 will surely encourage more along these lines in future years.

Monday, December 12, 2016

A short course on FATML -- and a longer course on ethics in data science.

I'm at IIIT Allahabad teaching a short course on fairness, accountability and transparency in machine learning. This is part of the GIAN program sponsored by the Government of India to enable Indian researchers from outside the country to come back and share their knowledge. The clever pun here is that GIAN -- which stands for Global Initiative of Academic Networks -- also means 'knowledge' in Hindi (ज्ञान).
Anyway, I'm excited to be teaching a course on this topic, and that has forced me to organize my thoughts on the material as well (which has already led to some interesting insights about how the literature fits together). I'm writing out my lecture notes, and one of the energetic students at the course has kindly offered to help me clean them out, so I hope to have a set of notes in less than a year or so. 

Note that the syllabus on the linked page is evolving: it will change over the course of the next few days as I add more things in. 

I'm also excited to to be teaching a (brand-new) undergraduate course on ethics and data science next fall. I'll have more to say about this as I prepare material, but any suggestions/materials are welcome. 

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